{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import locale\n",
    "from locale import atof\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.impute import SimpleImputer\n",
    "import sklearn.metrics as metrics\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\iqbal\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3071: DtypeWarning: Columns (11,61,62,66,67,77) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "path = \"C:/Users/iqbal/Desktop/Code/crunchbase test/Crunchbase Data\"\n",
    "all_files = glob.glob(os.path.join(path, \"*.csv\"))\n",
    "\n",
    "data = pd.read_csv('./combined_csv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',100)\n",
    "pd.set_option('display.max_rows',100)\n",
    "plt.rcParams[\"figure.figsize\"] = (20,5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Last Funding Amount Currency</th>\n",
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       "      <th>Last Funding Type</th>\n",
       "      <th>Last Equity Funding Amount</th>\n",
       "      <th>Last Equity Funding Amount Currency</th>\n",
       "      <th>Last Equity Funding Amount Currency (in USD)</th>\n",
       "      <th>Last Equity Funding Type</th>\n",
       "      <th>Total Equity Funding Amount</th>\n",
       "      <th>Total Equity Funding Amount Currency</th>\n",
       "      <th>Total Equity Funding Amount Currency (in USD)</th>\n",
       "      <th>Total Funding Amount</th>\n",
       "      <th>Total Funding Amount Currency</th>\n",
       "      <th>Total Funding Amount Currency (in USD)</th>\n",
       "      <th>Top 5 Investors</th>\n",
       "      <th>Number of Lead Investors</th>\n",
       "      <th>Number of Investors</th>\n",
       "      <th>Number of Acquisitions</th>\n",
       "      <th>Acquisition Status</th>\n",
       "      <th>IPO Status</th>\n",
       "      <th>IPO Date</th>\n",
       "      <th>Delisted Date</th>\n",
       "      <th>Delisted Date Precision</th>\n",
       "      <th>Money Raised at IPO</th>\n",
       "      <th>Money Raised at IPO Currency</th>\n",
       "      <th>Money Raised at IPO Currency (in USD)</th>\n",
       "      <th>Valuation at IPO</th>\n",
       "      <th>Valuation at IPO Currency</th>\n",
       "      <th>Valuation at IPO Currency (in USD)</th>\n",
       "      <th>Stock Symbol</th>\n",
       "      <th>Stock Symbol URL</th>\n",
       "      <th>Stock Exchange</th>\n",
       "      <th>IPqwery - Patents Granted</th>\n",
       "      <th>IPqwery - Trademarks Registered</th>\n",
       "      <th>IPqwery - Most Popular Patent Class</th>\n",
       "      <th>IPqwery - Most Popular Trademark Class</th>\n",
       "      <th>Investor Type</th>\n",
       "      <th>Accelerator Program Type</th>\n",
       "      <th>Accelerator Duration (in weeks)</th>\n",
       "      <th>Acquisition Type</th>\n",
       "      <th>SEMrush - Visit Duration Growth</th>\n",
       "      <th>SEMrush - Page Views / Visit Growth</th>\n",
       "      <th>SEMrush - Bounce Rate</th>\n",
       "      <th>SEMrush - Bounce Rate Growth</th>\n",
       "      <th>SEMrush - Global Traffic Rank</th>\n",
       "      <th>SEMrush - Monthly Rank Change (#)</th>\n",
       "      <th>SEMrush - Monthly Rank Growth</th>\n",
       "      <th>BuiltWith - Active Tech Count</th>\n",
       "      <th>G2 Stack - Total Products Active</th>\n",
       "      <th>Aberdeen - IT Spend</th>\n",
       "      <th>Aberdeen - IT Spend Currency</th>\n",
       "      <th>Aberdeen - IT Spend Currency (in USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Replicant</td>\n",
       "      <td>https://www.crunchbase.com/organization/replic...</td>\n",
       "      <td>Artificial Intelligence, Information Services,...</td>\n",
       "      <td>San Francisco, California, United States</td>\n",
       "      <td>1,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Active</td>\n",
       "      <td>4</td>\n",
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       "      <td>$1M to $10M</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>For Profit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Artificial Intelligence, Data and Analytics, I...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Andrew Abraham, Benjamin Gleitzman, Gadi Shamia</td>\n",
       "      <td>11-50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Early Stage Venture</td>\n",
       "      <td>2020-09-10</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>Series A</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>Series A</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>Norwest Venture Partners, Costanoa Ventures, B...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dataminr</td>\n",
       "      <td>https://www.crunchbase.com/organization/dataminr</td>\n",
       "      <td>Analytics, Artificial Intelligence, Enterprise...</td>\n",
       "      <td>New York, New York, United States</td>\n",
       "      <td>1,001</td>\n",
       "      <td>78,317</td>\n",
       "      <td>122,647.33</td>\n",
       "      <td>2.69%</td>\n",
       "      <td>3,503</td>\n",
       "      <td>2.46</td>\n",
       "      <td>Active</td>\n",
       "      <td>185</td>\n",
       "      <td>Greater New York Area, East Coast, Northeaster...</td>\n",
       "      <td>$50M to $100M</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>For Profit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Artificial Intelligence, Data and Analytics, G...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Jeff Kinsey, Sam Hendel, Theodore Bailey</td>\n",
       "      <td>251-500</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Late Stage Venture</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Secondary Market</td>\n",
       "      <td>391572500.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>391572500.0</td>\n",
       "      <td>Series E</td>\n",
       "      <td>577012621.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>577012621.0</td>\n",
       "      <td>577012621.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>577012621.0</td>\n",
       "      <td>Credit Suisse, BoxGroup, Wellington Management...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Computing; Calculating</td>\n",
       "      <td>Scientific and technological services</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-50.45%</td>\n",
       "      <td>-31.3%</td>\n",
       "      <td>60.04%</td>\n",
       "      <td>-0.52%</td>\n",
       "      <td>305,252</td>\n",
       "      <td>-1,264</td>\n",
       "      <td>-0.41%</td>\n",
       "      <td>52.0</td>\n",
       "      <td>37</td>\n",
       "      <td>2934192.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>2934192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Recorded Future</td>\n",
       "      <td>https://www.crunchbase.com/organization/record...</td>\n",
       "      <td>Analytics, Cyber Security, Machine Learning, R...</td>\n",
       "      <td>Somerville, Massachusetts, United States</td>\n",
       "      <td>1,002</td>\n",
       "      <td>163,982</td>\n",
       "      <td>169,072.33</td>\n",
       "      <td>12.49%</td>\n",
       "      <td>625</td>\n",
       "      <td>1.22</td>\n",
       "      <td>Active</td>\n",
       "      <td>402</td>\n",
       "      <td>Greater Boston Area, East Coast, New England</td>\n",
       "      <td>$1M to $10M</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>year</td>\n",
       "      <td>2019-05-30</td>\n",
       "      <td>day</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>For Profit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Artificial Intelligence, Data and Analytics, I...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Christopher Ahlberg, Erik Wistrand, Jan Sparud...</td>\n",
       "      <td>501-1000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>M&amp;A</td>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>Series E</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>Series E</td>\n",
       "      <td>57900000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>57900000.0</td>\n",
       "      <td>57900000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>57900000.0</td>\n",
       "      <td>GV, Insight Partners, Balderton Capital, Accom...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Was Acquired</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Acquisition</td>\n",
       "      <td>-27.16%</td>\n",
       "      <td>-49.98%</td>\n",
       "      <td>85.02%</td>\n",
       "      <td>19.18%</td>\n",
       "      <td>180,729</td>\n",
       "      <td>-13,466</td>\n",
       "      <td>-6.93%</td>\n",
       "      <td>82.0</td>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lilium</td>\n",
       "      <td>https://www.crunchbase.com/organization/lilium...</td>\n",
       "      <td>Air Transportation, Automotive, Transportation...</td>\n",
       "      <td>Weßling, Bayern, Germany</td>\n",
       "      <td>1,003</td>\n",
       "      <td>6,914</td>\n",
       "      <td>52,035.83</td>\n",
       "      <td>-14.69%</td>\n",
       "      <td>349</td>\n",
       "      <td>3.71</td>\n",
       "      <td>Active</td>\n",
       "      <td>72</td>\n",
       "      <td>European Union (EU)</td>\n",
       "      <td>$100M to $500M</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>For Profit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Transportation, Travel and Tourism</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Daniel Wiegand, Matthias Meiner, Patrick Nathe...</td>\n",
       "      <td>251-500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>Venture - Series Unknown</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>Venture - Series Unknown</td>\n",
       "      <td>376400000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>376400000.0</td>\n",
       "      <td>376400000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>376400000.0</td>\n",
       "      <td>Tencent Holdings, Atomico, Baillie Gifford, Ob...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>Aircraft; Aviation; Cosmonautics</td>\n",
       "      <td>Transport, packaging and storing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.34%</td>\n",
       "      <td>4.74%</td>\n",
       "      <td>65.18%</td>\n",
       "      <td>25.68%</td>\n",
       "      <td>1,575,844</td>\n",
       "      <td>202,421</td>\n",
       "      <td>14.74%</td>\n",
       "      <td>43.0</td>\n",
       "      <td>22</td>\n",
       "      <td>2485.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>2485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sensor Tower</td>\n",
       "      <td>https://www.crunchbase.com/organization/sensor...</td>\n",
       "      <td>Analytics, Android, Big Data, iOS, Market Rese...</td>\n",
       "      <td>San Francisco, California, United States</td>\n",
       "      <td>1,004</td>\n",
       "      <td>1,547,366</td>\n",
       "      <td>1,380,072.83</td>\n",
       "      <td>-1.86%</td>\n",
       "      <td>725</td>\n",
       "      <td>3.14</td>\n",
       "      <td>Active</td>\n",
       "      <td>610</td>\n",
       "      <td>San Francisco Bay Area, West Coast, Western US</td>\n",
       "      <td>$10M to $50M</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>For Profit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apps, Data and Analytics, Design, Mobile, Plat...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Alex Malafeev, Oliver Yeh</td>\n",
       "      <td>101-250</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>Private Equity</td>\n",
       "      <td>46000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>46000000.0</td>\n",
       "      <td>46000000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>46000000.0</td>\n",
       "      <td>Pear VC, Riverwood Capital, AngelPad, BDMI, Me...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Made Acquisitions</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.83%</td>\n",
       "      <td>16.9%</td>\n",
       "      <td>42.17%</td>\n",
       "      <td>7.8%</td>\n",
       "      <td>30,665</td>\n",
       "      <td>565</td>\n",
       "      <td>1.88%</td>\n",
       "      <td>51.0</td>\n",
       "      <td>30</td>\n",
       "      <td>44843.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>44843.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Organization Name                              Organization Name URL  \\\n",
       "0         Replicant  https://www.crunchbase.com/organization/replic...   \n",
       "1          Dataminr   https://www.crunchbase.com/organization/dataminr   \n",
       "2   Recorded Future  https://www.crunchbase.com/organization/record...   \n",
       "3            Lilium  https://www.crunchbase.com/organization/lilium...   \n",
       "4      Sensor Tower  https://www.crunchbase.com/organization/sensor...   \n",
       "\n",
       "                                          Industries  \\\n",
       "0  Artificial Intelligence, Information Services,...   \n",
       "1  Analytics, Artificial Intelligence, Enterprise...   \n",
       "2  Analytics, Cyber Security, Machine Learning, R...   \n",
       "3  Air Transportation, Automotive, Transportation...   \n",
       "4  Analytics, Android, Big Data, iOS, Market Rese...   \n",
       "\n",
       "                      Headquarters Location CB Rank (Company)  \\\n",
       "0  San Francisco, California, United States             1,000   \n",
       "1         New York, New York, United States             1,001   \n",
       "2  Somerville, Massachusetts, United States             1,002   \n",
       "3                  Weßling, Bayern, Germany             1,003   \n",
       "4  San Francisco, California, United States             1,004   \n",
       "\n",
       "  SEMrush - Monthly Visits SEMrush - Average Visits (6 months)  \\\n",
       "0                      NaN                                 NaN   \n",
       "1                   78,317                          122,647.33   \n",
       "2                  163,982                          169,072.33   \n",
       "3                    6,914                           52,035.83   \n",
       "4                1,547,366                        1,380,072.83   \n",
       "\n",
       "  SEMrush - Monthly Visits Growth SEMrush - Visit Duration  \\\n",
       "0                             NaN                      NaN   \n",
       "1                           2.69%                    3,503   \n",
       "2                          12.49%                      625   \n",
       "3                         -14.69%                      349   \n",
       "4                          -1.86%                      725   \n",
       "\n",
       "   SEMrush - Page Views / Visit Operating Status Number of Articles  \\\n",
       "0                           NaN           Active                  4   \n",
       "1                          2.46           Active                185   \n",
       "2                          1.22           Active                402   \n",
       "3                          3.71           Active                 72   \n",
       "4                          3.14           Active                610   \n",
       "\n",
       "                                Headquarters Regions Estimated Revenue Range  \\\n",
       "0     San Francisco Bay Area, West Coast, Western US             $1M to $10M   \n",
       "1  Greater New York Area, East Coast, Northeaster...           $50M to $100M   \n",
       "2       Greater Boston Area, East Coast, New England             $1M to $10M   \n",
       "3                                European Union (EU)          $100M to $500M   \n",
       "4     San Francisco Bay Area, West Coast, Western US            $10M to $50M   \n",
       "\n",
       "  Founded Date Founded Date Precision   Exit Date Exit Date Precision  \\\n",
       "0   2017-01-01                   year         NaN                 NaN   \n",
       "1   2009-01-01                   year         NaN                 NaN   \n",
       "2   2009-01-01                   year  2019-05-30                 day   \n",
       "3   2015-01-01                   year         NaN                 NaN   \n",
       "4   2013-01-01                   year         NaN                 NaN   \n",
       "\n",
       "  Closed Date Closed Date Precision Company Type Investment Stage  \\\n",
       "0         NaN                   NaN   For Profit              NaN   \n",
       "1         NaN                   NaN   For Profit              NaN   \n",
       "2         NaN                   NaN   For Profit              NaN   \n",
       "3         NaN                   NaN   For Profit              NaN   \n",
       "4         NaN                   NaN   For Profit              NaN   \n",
       "\n",
       "                                     Industry Groups  Number of Founders  \\\n",
       "0  Artificial Intelligence, Data and Analytics, I...                 3.0   \n",
       "1  Artificial Intelligence, Data and Analytics, G...                 3.0   \n",
       "2  Artificial Intelligence, Data and Analytics, I...                 4.0   \n",
       "3                 Transportation, Travel and Tourism                 4.0   \n",
       "4  Apps, Data and Analytics, Design, Mobile, Plat...                 2.0   \n",
       "\n",
       "                                            Founders Number of Employees  \\\n",
       "0    Andrew Abraham, Benjamin Gleitzman, Gadi Shamia               11-50   \n",
       "1           Jeff Kinsey, Sam Hendel, Theodore Bailey             251-500   \n",
       "2  Christopher Ahlberg, Erik Wistrand, Jan Sparud...            501-1000   \n",
       "3  Daniel Wiegand, Matthias Meiner, Patrick Nathe...             251-500   \n",
       "4                          Alex Malafeev, Oliver Yeh             101-250   \n",
       "\n",
       "   Number of Funding Rounds       Funding Status Last Funding Date  \\\n",
       "0                       3.0  Early Stage Venture        2020-09-10   \n",
       "1                      11.0   Late Stage Venture        2020-01-05   \n",
       "2                       5.0                  M&A        2017-10-31   \n",
       "3                       5.0                  NaN        2020-06-09   \n",
       "4                       3.0       Private Equity        2020-05-04   \n",
       "\n",
       "   Last Funding Amount Last Funding Amount Currency  \\\n",
       "0           27000000.0                          USD   \n",
       "1                  NaN                          NaN   \n",
       "2           25000000.0                          USD   \n",
       "3           35000000.0                          USD   \n",
       "4           45000000.0                          USD   \n",
       "\n",
       "   Last Funding Amount Currency (in USD)         Last Funding Type  \\\n",
       "0                             27000000.0                  Series A   \n",
       "1                                    NaN          Secondary Market   \n",
       "2                             25000000.0                  Series E   \n",
       "3                             35000000.0  Venture - Series Unknown   \n",
       "4                             45000000.0            Private Equity   \n",
       "\n",
       "   Last Equity Funding Amount Last Equity Funding Amount Currency  \\\n",
       "0                  27000000.0                                 USD   \n",
       "1                 391572500.0                                 USD   \n",
       "2                  25000000.0                                 USD   \n",
       "3                  35000000.0                                 USD   \n",
       "4                  45000000.0                                 USD   \n",
       "\n",
       "   Last Equity Funding Amount Currency (in USD)  Last Equity Funding Type  \\\n",
       "0                                    27000000.0                  Series A   \n",
       "1                                   391572500.0                  Series E   \n",
       "2                                    25000000.0                  Series E   \n",
       "3                                    35000000.0  Venture - Series Unknown   \n",
       "4                                    45000000.0            Private Equity   \n",
       "\n",
       "   Total Equity Funding Amount Total Equity Funding Amount Currency  \\\n",
       "0                   35000000.0                                  USD   \n",
       "1                  577012621.0                                  USD   \n",
       "2                   57900000.0                                  USD   \n",
       "3                  376400000.0                                  USD   \n",
       "4                   46000000.0                                  USD   \n",
       "\n",
       "   Total Equity Funding Amount Currency (in USD)  Total Funding Amount  \\\n",
       "0                                     35000000.0            35000000.0   \n",
       "1                                    577012621.0           577012621.0   \n",
       "2                                     57900000.0            57900000.0   \n",
       "3                                    376400000.0           376400000.0   \n",
       "4                                     46000000.0            46000000.0   \n",
       "\n",
       "  Total Funding Amount Currency  Total Funding Amount Currency (in USD)  \\\n",
       "0                           USD                              35000000.0   \n",
       "1                           USD                             577012621.0   \n",
       "2                           USD                              57900000.0   \n",
       "3                           USD                             376400000.0   \n",
       "4                           USD                              46000000.0   \n",
       "\n",
       "                                     Top 5 Investors  \\\n",
       "0  Norwest Venture Partners, Costanoa Ventures, B...   \n",
       "1  Credit Suisse, BoxGroup, Wellington Management...   \n",
       "2  GV, Insight Partners, Balderton Capital, Accom...   \n",
       "3  Tencent Holdings, Atomico, Baillie Gifford, Ob...   \n",
       "4  Pear VC, Riverwood Capital, AngelPad, BDMI, Me...   \n",
       "\n",
       "   Number of Lead Investors  Number of Investors  Number of Acquisitions  \\\n",
       "0                       2.0                  5.0                     NaN   \n",
       "1                       3.0                 27.0                     NaN   \n",
       "2                       4.0                  8.0                     NaN   \n",
       "3                       3.0                  6.0                     NaN   \n",
       "4                       2.0                 10.0                     1.0   \n",
       "\n",
       "  Acquisition Status IPO Status IPO Date Delisted Date  \\\n",
       "0                NaN    Private      NaN           NaN   \n",
       "1                NaN    Private      NaN           NaN   \n",
       "2       Was Acquired    Private      NaN           NaN   \n",
       "3                NaN    Private      NaN           NaN   \n",
       "4  Made Acquisitions    Private      NaN           NaN   \n",
       "\n",
       "  Delisted Date Precision  Money Raised at IPO Money Raised at IPO Currency  \\\n",
       "0                     NaN                  NaN                          NaN   \n",
       "1                     NaN                  NaN                          NaN   \n",
       "2                     NaN                  NaN                          NaN   \n",
       "3                     NaN                  NaN                          NaN   \n",
       "4                     NaN                  NaN                          NaN   \n",
       "\n",
       "   Money Raised at IPO Currency (in USD)  Valuation at IPO  \\\n",
       "0                                    NaN               NaN   \n",
       "1                                    NaN               NaN   \n",
       "2                                    NaN               NaN   \n",
       "3                                    NaN               NaN   \n",
       "4                                    NaN               NaN   \n",
       "\n",
       "  Valuation at IPO Currency  Valuation at IPO Currency (in USD) Stock Symbol  \\\n",
       "0                       NaN                                 NaN          NaN   \n",
       "1                       NaN                                 NaN          NaN   \n",
       "2                       NaN                                 NaN          NaN   \n",
       "3                       NaN                                 NaN          NaN   \n",
       "4                       NaN                                 NaN          NaN   \n",
       "\n",
       "  Stock Symbol URL Stock Exchange IPqwery - Patents Granted  \\\n",
       "0              NaN            NaN                       NaN   \n",
       "1              NaN            NaN                         1   \n",
       "2              NaN            NaN                       NaN   \n",
       "3              NaN            NaN                         3   \n",
       "4              NaN            NaN                       NaN   \n",
       "\n",
       "  IPqwery - Trademarks Registered IPqwery - Most Popular Patent Class  \\\n",
       "0                             NaN                                 NaN   \n",
       "1                               5              Computing; Calculating   \n",
       "2                             NaN                                 NaN   \n",
       "3                               7    Aircraft; Aviation; Cosmonautics   \n",
       "4                             NaN                                 NaN   \n",
       "\n",
       "  IPqwery - Most Popular Trademark Class Investor Type  \\\n",
       "0                                    NaN           NaN   \n",
       "1  Scientific and technological services           NaN   \n",
       "2                                    NaN           NaN   \n",
       "3       Transport, packaging and storing           NaN   \n",
       "4                                    NaN           NaN   \n",
       "\n",
       "  Accelerator Program Type Accelerator Duration (in weeks) Acquisition Type  \\\n",
       "0                      NaN                             NaN              NaN   \n",
       "1                      NaN                             NaN              NaN   \n",
       "2                      NaN                             NaN      Acquisition   \n",
       "3                      NaN                             NaN              NaN   \n",
       "4                      NaN                             NaN              NaN   \n",
       "\n",
       "  SEMrush - Visit Duration Growth SEMrush - Page Views / Visit Growth  \\\n",
       "0                             NaN                                 NaN   \n",
       "1                         -50.45%                              -31.3%   \n",
       "2                         -27.16%                             -49.98%   \n",
       "3                          20.34%                               4.74%   \n",
       "4                          19.83%                               16.9%   \n",
       "\n",
       "  SEMrush - Bounce Rate SEMrush - Bounce Rate Growth  \\\n",
       "0                   NaN                          NaN   \n",
       "1                60.04%                       -0.52%   \n",
       "2                85.02%                       19.18%   \n",
       "3                65.18%                       25.68%   \n",
       "4                42.17%                         7.8%   \n",
       "\n",
       "  SEMrush - Global Traffic Rank SEMrush - Monthly Rank Change (#)  \\\n",
       "0                           NaN                               NaN   \n",
       "1                       305,252                            -1,264   \n",
       "2                       180,729                           -13,466   \n",
       "3                     1,575,844                           202,421   \n",
       "4                        30,665                               565   \n",
       "\n",
       "  SEMrush - Monthly Rank Growth  BuiltWith - Active Tech Count  \\\n",
       "0                           NaN                           49.0   \n",
       "1                        -0.41%                           52.0   \n",
       "2                        -6.93%                           82.0   \n",
       "3                        14.74%                           43.0   \n",
       "4                         1.88%                           51.0   \n",
       "\n",
       "  G2 Stack - Total Products Active  Aberdeen - IT Spend  \\\n",
       "0                               30                  NaN   \n",
       "1                               37            2934192.0   \n",
       "2                               44                  NaN   \n",
       "3                               22               2485.0   \n",
       "4                               30              44843.0   \n",
       "\n",
       "  Aberdeen - IT Spend Currency  Aberdeen - IT Spend Currency (in USD)  \n",
       "0                          NaN                                    NaN  \n",
       "1                          USD                              2934192.0  \n",
       "2                          NaN                                    NaN  \n",
       "3                          USD                                 2485.0  \n",
       "4                          USD                                44843.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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  {
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   "execution_count": 28,
   "metadata": {},
   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 153256 entries, 0 to 153255\n",
      "Data columns (total 81 columns):\n",
      " #   Column                                         Non-Null Count   Dtype  \n",
      "---  ------                                         --------------   -----  \n",
      " 0   Organization Name                              153255 non-null  object \n",
      " 1   Organization Name URL                          153256 non-null  object \n",
      " 2   Industries                                     150875 non-null  object \n",
      " 3   Headquarters Location                          140502 non-null  object \n",
      " 4   CB Rank (Company)                              153256 non-null  object \n",
      " 5   SEMrush - Monthly Visits                       59505 non-null   object \n",
      " 6   SEMrush - Average Visits (6 months)            52198 non-null   object \n",
      " 7   SEMrush - Monthly Visits Growth                55562 non-null   object \n",
      " 8   SEMrush - Visit Duration                       59505 non-null   object \n",
      " 9   SEMrush - Page Views / Visit                   59505 non-null   float64\n",
      " 10  Operating Status                               153256 non-null  object \n",
      " 11  Number of Articles                             76246 non-null   object \n",
      " 12  Headquarters Regions                           119810 non-null  object \n",
      " 13  Estimated Revenue Range                        76194 non-null   object \n",
      " 14  Founded Date                                   144468 non-null  object \n",
      " 15  Founded Date Precision                         144468 non-null  object \n",
      " 16  Exit Date                                      21397 non-null   object \n",
      " 17  Exit Date Precision                            21397 non-null   object \n",
      " 18  Closed Date                                    12575 non-null   object \n",
      " 19  Closed Date Precision                          35860 non-null   object \n",
      " 20  Company Type                                   151016 non-null  object \n",
      " 21  Investment Stage                               779 non-null     object \n",
      " 22  Industry Groups                                150872 non-null  object \n",
      " 23  Number of Founders                             95572 non-null   float64\n",
      " 24  Founders                                       95572 non-null   object \n",
      " 25  Number of Employees                            140746 non-null  object \n",
      " 26  Number of Funding Rounds                       115715 non-null  float64\n",
      " 27  Funding Status                                 88785 non-null   object \n",
      " 28  Last Funding Date                              115710 non-null  object \n",
      " 29  Last Funding Amount                            100986 non-null  float64\n",
      " 30  Last Funding Amount Currency                   100992 non-null  object \n",
      " 31  Last Funding Amount Currency (in USD)          100986 non-null  float64\n",
      " 32  Last Funding Type                              115710 non-null  object \n",
      " 33  Last Equity Funding Amount                     92736 non-null   float64\n",
      " 34  Last Equity Funding Amount Currency            92741 non-null   object \n",
      " 35  Last Equity Funding Amount Currency (in USD)   92736 non-null   float64\n",
      " 36  Last Equity Funding Type                       107005 non-null  object \n",
      " 37  Total Equity Funding Amount                    100129 non-null  float64\n",
      " 38  Total Equity Funding Amount Currency           100132 non-null  object \n",
      " 39  Total Equity Funding Amount Currency (in USD)  100128 non-null  float64\n",
      " 40  Total Funding Amount                           109766 non-null  float64\n",
      " 41  Total Funding Amount Currency                  109770 non-null  object \n",
      " 42  Total Funding Amount Currency (in USD)         109765 non-null  float64\n",
      " 43  Top 5 Investors                                88252 non-null   object \n",
      " 44  Number of Lead Investors                       56448 non-null   float64\n",
      " 45  Number of Investors                            88254 non-null   float64\n",
      " 46  Number of Acquisitions                         13179 non-null   float64\n",
      " 47  Acquisition Status                             24247 non-null   object \n",
      " 48  IPO Status                                     153256 non-null  object \n",
      " 49  IPO Date                                       8171 non-null    object \n",
      " 50  Delisted Date                                  850 non-null     object \n",
      " 51  Delisted Date Precision                        1175 non-null    object \n",
      " 52  Money Raised at IPO                            2348 non-null    float64\n",
      " 53  Money Raised at IPO Currency                   2349 non-null    object \n",
      " 54  Money Raised at IPO Currency (in USD)          2348 non-null    float64\n",
      " 55  Valuation at IPO                               1420 non-null    float64\n",
      " 56  Valuation at IPO Currency                      1420 non-null    object \n",
      " 57  Valuation at IPO Currency (in USD)             1420 non-null    float64\n",
      " 58  Stock Symbol                                   8164 non-null    object \n",
      " 59  Stock Symbol URL                               8172 non-null    object \n",
      " 60  Stock Exchange                                 8162 non-null    object \n",
      " 61  IPqwery - Patents Granted                      47243 non-null   object \n",
      " 62  IPqwery - Trademarks Registered                47243 non-null   object \n",
      " 63  IPqwery - Most Popular Patent Class            21930 non-null   object \n",
      " 64  IPqwery - Most Popular Trademark Class         40962 non-null   object \n",
      " 65  Investor Type                                  1378 non-null    object \n",
      " 66  Accelerator Program Type                       65 non-null      object \n",
      " 67  Accelerator Duration (in weeks)                51 non-null      object \n",
      " 68  Acquisition Type                               13242 non-null   object \n",
      " 69  SEMrush - Visit Duration Growth                43969 non-null   object \n",
      " 70  SEMrush - Page Views / Visit Growth            55562 non-null   object \n",
      " 71  SEMrush - Bounce Rate                          59505 non-null   object \n",
      " 72  SEMrush - Bounce Rate Growth                   50848 non-null   object \n",
      " 73  SEMrush - Global Traffic Rank                  59505 non-null   object \n",
      " 74  SEMrush - Monthly Rank Change (#)              55562 non-null   object \n",
      " 75  SEMrush - Monthly Rank Growth                  55562 non-null   object \n",
      " 76  BuiltWith - Active Tech Count                  138310 non-null  float64\n",
      " 77  G2 Stack - Total Products Active               76114 non-null   object \n",
      " 78  Aberdeen - IT Spend                            21107 non-null   float64\n",
      " 79  Aberdeen - IT Spend Currency                   21692 non-null   object \n",
      " 80  Aberdeen - IT Spend Currency (in USD)          21107 non-null   float64\n",
      "dtypes: float64(21), object(60)\n",
      "memory usage: 94.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEMrush - Page Views / Visit</th>\n",
       "      <th>Number of Founders</th>\n",
       "      <th>Number of Funding Rounds</th>\n",
       "      <th>Last Funding Amount</th>\n",
       "      <th>Last Funding Amount Currency (in USD)</th>\n",
       "      <th>Last Equity Funding Amount</th>\n",
       "      <th>Last Equity Funding Amount Currency (in USD)</th>\n",
       "      <th>Total Equity Funding Amount</th>\n",
       "      <th>Total Equity Funding Amount Currency (in USD)</th>\n",
       "      <th>Total Funding Amount</th>\n",
       "      <th>Total Funding Amount Currency (in USD)</th>\n",
       "      <th>Number of Lead Investors</th>\n",
       "      <th>Number of Investors</th>\n",
       "      <th>Number of Acquisitions</th>\n",
       "      <th>Money Raised at IPO</th>\n",
       "      <th>Money Raised at IPO Currency (in USD)</th>\n",
       "      <th>Valuation at IPO</th>\n",
       "      <th>Valuation at IPO Currency (in USD)</th>\n",
       "      <th>BuiltWith - Active Tech Count</th>\n",
       "      <th>Aberdeen - IT Spend</th>\n",
       "      <th>Aberdeen - IT Spend Currency (in USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>59505.000000</td>\n",
       "      <td>95572.000000</td>\n",
       "      <td>115715.000000</td>\n",
       "      <td>1.009860e+05</td>\n",
       "      <td>1.009860e+05</td>\n",
       "      <td>9.273600e+04</td>\n",
       "      <td>9.273600e+04</td>\n",
       "      <td>1.001290e+05</td>\n",
       "      <td>1.001280e+05</td>\n",
       "      <td>1.097660e+05</td>\n",
       "      <td>1.097650e+05</td>\n",
       "      <td>56448.000000</td>\n",
       "      <td>88254.000000</td>\n",
       "      <td>13179.000000</td>\n",
       "      <td>2.348000e+03</td>\n",
       "      <td>2.348000e+03</td>\n",
       "      <td>1.420000e+03</td>\n",
       "      <td>1.420000e+03</td>\n",
       "      <td>138310.000000</td>\n",
       "      <td>2.110700e+04</td>\n",
       "      <td>2.110700e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.872548</td>\n",
       "      <td>1.847277</td>\n",
       "      <td>2.273188</td>\n",
       "      <td>2.453704e+08</td>\n",
       "      <td>1.994465e+07</td>\n",
       "      <td>2.058825e+08</td>\n",
       "      <td>1.643864e+07</td>\n",
       "      <td>2.075384e+08</td>\n",
       "      <td>2.749571e+07</td>\n",
       "      <td>2.385137e+08</td>\n",
       "      <td>3.341671e+07</td>\n",
       "      <td>1.733702</td>\n",
       "      <td>3.757835</td>\n",
       "      <td>4.079217</td>\n",
       "      <td>4.132929e+08</td>\n",
       "      <td>2.649868e+08</td>\n",
       "      <td>2.835866e+09</td>\n",
       "      <td>1.640049e+09</td>\n",
       "      <td>27.432731</td>\n",
       "      <td>2.193506e+07</td>\n",
       "      <td>2.193506e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.242115</td>\n",
       "      <td>1.015553</td>\n",
       "      <td>1.870581</td>\n",
       "      <td>2.035526e+10</td>\n",
       "      <td>3.710427e+08</td>\n",
       "      <td>1.894404e+10</td>\n",
       "      <td>1.845099e+08</td>\n",
       "      <td>1.831216e+10</td>\n",
       "      <td>2.596477e+08</td>\n",
       "      <td>1.954193e+10</td>\n",
       "      <td>4.366545e+08</td>\n",
       "      <td>1.230391</td>\n",
       "      <td>4.205362</td>\n",
       "      <td>9.801255</td>\n",
       "      <td>2.620517e+09</td>\n",
       "      <td>1.039915e+09</td>\n",
       "      <td>1.569973e+10</td>\n",
       "      <td>5.202418e+09</td>\n",
       "      <td>22.913225</td>\n",
       "      <td>3.228889e+08</td>\n",
       "      <td>3.228889e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>5.000000e+02</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.520000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.520000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.500000e+05</td>\n",
       "      <td>3.500000e+05</td>\n",
       "      <td>7.000000e+05</td>\n",
       "      <td>6.991440e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.190000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000e+05</td>\n",
       "      <td>3.550000e+05</td>\n",
       "      <td>5.000000e+05</td>\n",
       "      <td>4.486538e+05</td>\n",
       "      <td>6.131500e+05</td>\n",
       "      <td>5.073128e+05</td>\n",
       "      <td>5.300000e+05</td>\n",
       "      <td>4.999250e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.635000e+07</td>\n",
       "      <td>4.500000e+07</td>\n",
       "      <td>2.102550e+08</td>\n",
       "      <td>2.023241e+08</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.323700e+04</td>\n",
       "      <td>3.323700e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000e+06</td>\n",
       "      <td>1.550000e+06</td>\n",
       "      <td>2.400000e+06</td>\n",
       "      <td>1.750000e+06</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>2.405715e+06</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>2.251679e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.325000e+07</td>\n",
       "      <td>9.000000e+07</td>\n",
       "      <td>4.776875e+08</td>\n",
       "      <td>4.495709e+08</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.137210e+05</td>\n",
       "      <td>1.137210e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.120000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>6.801311e+06</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>7.186733e+06</td>\n",
       "      <td>1.558974e+07</td>\n",
       "      <td>1.110462e+07</td>\n",
       "      <td>1.508875e+07</td>\n",
       "      <td>1.100000e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.097750e+08</td>\n",
       "      <td>1.932500e+08</td>\n",
       "      <td>1.400000e+09</td>\n",
       "      <td>1.212318e+09</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>6.477790e+05</td>\n",
       "      <td>6.477790e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>356.830000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>4.000000e+12</td>\n",
       "      <td>1.000000e+11</td>\n",
       "      <td>4.000000e+12</td>\n",
       "      <td>2.400000e+10</td>\n",
       "      <td>4.000000e+12</td>\n",
       "      <td>2.992950e+10</td>\n",
       "      <td>4.000000e+12</td>\n",
       "      <td>1.000000e+11</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>8.800000e+10</td>\n",
       "      <td>2.560000e+10</td>\n",
       "      <td>4.051500e+11</td>\n",
       "      <td>1.040000e+11</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>1.812905e+10</td>\n",
       "      <td>1.812905e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SEMrush - Page Views / Visit  Number of Founders  \\\n",
       "count                  59505.000000        95572.000000   \n",
       "mean                       2.872548            1.847277   \n",
       "std                        5.242115            1.015553   \n",
       "min                        1.000000            0.000000   \n",
       "25%                        1.190000            1.000000   \n",
       "50%                        2.000000            2.000000   \n",
       "75%                        3.120000            2.000000   \n",
       "max                      356.830000           18.000000   \n",
       "\n",
       "       Number of Funding Rounds  Last Funding Amount  \\\n",
       "count             115715.000000         1.009860e+05   \n",
       "mean                   2.273188         2.453704e+08   \n",
       "std                    1.870581         2.035526e+10   \n",
       "min                    0.000000         2.500000e+02   \n",
       "25%                    1.000000         4.000000e+05   \n",
       "50%                    2.000000         2.000000e+06   \n",
       "75%                    3.000000         1.000000e+07   \n",
       "max                   41.000000         4.000000e+12   \n",
       "\n",
       "       Last Funding Amount Currency (in USD)  Last Equity Funding Amount  \\\n",
       "count                           1.009860e+05                9.273600e+04   \n",
       "mean                            1.994465e+07                2.058825e+08   \n",
       "std                             3.710427e+08                1.894404e+10   \n",
       "min                             8.200000e+01                5.000000e+02   \n",
       "25%                             3.550000e+05                5.000000e+05   \n",
       "50%                             1.550000e+06                2.400000e+06   \n",
       "75%                             6.801311e+06                1.000000e+07   \n",
       "max                             1.000000e+11                4.000000e+12   \n",
       "\n",
       "       Last Equity Funding Amount Currency (in USD)  \\\n",
       "count                                  9.273600e+04   \n",
       "mean                                   1.643864e+07   \n",
       "std                                    1.845099e+08   \n",
       "min                                    8.200000e+01   \n",
       "25%                                    4.486538e+05   \n",
       "50%                                    1.750000e+06   \n",
       "75%                                    7.186733e+06   \n",
       "max                                    2.400000e+10   \n",
       "\n",
       "       Total Equity Funding Amount  \\\n",
       "count                 1.001290e+05   \n",
       "mean                  2.075384e+08   \n",
       "std                   1.831216e+10   \n",
       "min                   1.000000e+00   \n",
       "25%                   6.131500e+05   \n",
       "50%                   3.000000e+06   \n",
       "75%                   1.558974e+07   \n",
       "max                   4.000000e+12   \n",
       "\n",
       "       Total Equity Funding Amount Currency (in USD)  Total Funding Amount  \\\n",
       "count                                   1.001280e+05          1.097660e+05   \n",
       "mean                                    2.749571e+07          2.385137e+08   \n",
       "std                                     2.596477e+08          1.954193e+10   \n",
       "min                                     3.520000e+02          1.000000e+00   \n",
       "25%                                     5.073128e+05          5.300000e+05   \n",
       "50%                                     2.405715e+06          3.000000e+06   \n",
       "75%                                     1.110462e+07          1.508875e+07   \n",
       "max                                     2.992950e+10          4.000000e+12   \n",
       "\n",
       "       Total Funding Amount Currency (in USD)  Number of Lead Investors  \\\n",
       "count                            1.097650e+05              56448.000000   \n",
       "mean                             3.341671e+07                  1.733702   \n",
       "std                              4.366545e+08                  1.230391   \n",
       "min                              3.520000e+02                  0.000000   \n",
       "25%                              4.999250e+05                  1.000000   \n",
       "50%                              2.251679e+06                  1.000000   \n",
       "75%                              1.100000e+07                  2.000000   \n",
       "max                              1.000000e+11                 23.000000   \n",
       "\n",
       "       Number of Investors  Number of Acquisitions  Money Raised at IPO  \\\n",
       "count         88254.000000            13179.000000         2.348000e+03   \n",
       "mean              3.757835                4.079217         4.132929e+08   \n",
       "std               4.205362                9.801255         2.620517e+09   \n",
       "min               0.000000                0.000000         3.500000e+05   \n",
       "25%               1.000000                1.000000         4.635000e+07   \n",
       "50%               2.000000                2.000000         9.325000e+07   \n",
       "75%               5.000000                3.000000         2.097750e+08   \n",
       "max             108.000000              304.000000         8.800000e+10   \n",
       "\n",
       "       Money Raised at IPO Currency (in USD)  Valuation at IPO  \\\n",
       "count                           2.348000e+03      1.420000e+03   \n",
       "mean                            2.649868e+08      2.835866e+09   \n",
       "std                             1.039915e+09      1.569973e+10   \n",
       "min                             3.500000e+05      7.000000e+05   \n",
       "25%                             4.500000e+07      2.102550e+08   \n",
       "50%                             9.000000e+07      4.776875e+08   \n",
       "75%                             1.932500e+08      1.400000e+09   \n",
       "max                             2.560000e+10      4.051500e+11   \n",
       "\n",
       "       Valuation at IPO Currency (in USD)  BuiltWith - Active Tech Count  \\\n",
       "count                        1.420000e+03                  138310.000000   \n",
       "mean                         1.640049e+09                      27.432731   \n",
       "std                          5.202418e+09                      22.913225   \n",
       "min                          6.991440e+05                       0.000000   \n",
       "25%                          2.023241e+08                      10.000000   \n",
       "50%                          4.495709e+08                      22.000000   \n",
       "75%                          1.212318e+09                      38.000000   \n",
       "max                          1.040000e+11                     279.000000   \n",
       "\n",
       "       Aberdeen - IT Spend  Aberdeen - IT Spend Currency (in USD)  \n",
       "count         2.110700e+04                           2.110700e+04  \n",
       "mean          2.193506e+07                           2.193506e+07  \n",
       "std           3.228889e+08                           3.228889e+08  \n",
       "min           1.000000e+00                           1.000000e+00  \n",
       "25%           3.323700e+04                           3.323700e+04  \n",
       "50%           1.137210e+05                           1.137210e+05  \n",
       "75%           6.477790e+05                           6.477790e+05  \n",
       "max           1.812905e+10                           1.812905e+10  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Data and Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfMain = data[[\"Organization Name\",'Number of Articles', 'Headquarters Regions',\n",
    "               'Estimated Revenue Range', 'Industry Groups', 'Number of Founders', \"Founded Date\",\n",
    "               'Number of Employees', 'Number of Funding Rounds',\n",
    "               'Last Funding Amount Currency (in USD)', \n",
    "               'Last Equity Funding Amount Currency (in USD)',\n",
    "               'Last Equity Funding Type',\n",
    "               'Total Equity Funding Amount Currency (in USD)',\n",
    "               'Total Funding Amount Currency (in USD)', 'Number of Lead Investors',\n",
    "               'Number of Investors', 'BuiltWith - Active Tech Count', \"SEMrush - Monthly Visits\",\n",
    "               'SEMrush - Average Visits (6 months)', \"SEMrush - Visit Duration\", \"SEMrush - Page Views / Visit\",\n",
    "              ]]\n",
    "\n",
    "\n",
    "dfMain=dfMain.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfMain['Date 2'] = pd.to_datetime(dfMain[\"Founded Date\"], errors=\"coerce\")\n",
    "dfMain[\"Date 3\"] = pd.DatetimeIndex(dfMain[\"Date 2\"]).year\n",
    "dfMain[\"Years Active\"] = 2020  - dfMain[\"Date 3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$1M to $10M       5084\n",
       "$10M to $50M      2302\n",
       "Less than $1M     2204\n",
       "$100M to $500M     663\n",
       "$50M to $100M      568\n",
       "$1B to $10B        142\n",
       "$500M to $1B       135\n",
       "$10B+               33\n",
       "Name: Estimated Revenue Range, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMain[\"Estimated Revenue Range\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfMain[\"Estimated Revenue Range\"].value_counts()\n",
    "BigCompanies = dfMain.loc[dfMain[\"Estimated Revenue Range\"] == \"$10B+\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "San Francisco Bay Area, West Coast, Western US                                         1484\n",
       "European Union (EU)                                                                    1460\n",
       "Greater New York Area, East Coast, Northeastern US                                     1119\n",
       "Asia-Pacific (APAC)                                                                    1098\n",
       "San Francisco Bay Area, Silicon Valley, West Coast                                      925\n",
       "Greater Boston Area, East Coast, New England                                            542\n",
       "Greater Los Angeles Area, West Coast, Western US                                        512\n",
       "European Union (EU), Nordic Countries, Scandinavia                                      353\n",
       "Asia-Pacific (APAC), Association of Southeast Asian Nations (ASEAN), Southeast Asia     292\n",
       "Southern US                                                                             258\n",
       "Latin America                                                                           249\n",
       "Greater Seattle Area, West Coast, Western US                                            237\n",
       "Great Lakes                                                                             225\n",
       "Asia-Pacific (APAC), Australasia                                                        221\n",
       "Greater Chicago Area, Great Lakes, Midwestern US                                        202\n",
       "Greater Denver Area, Western US                                                         161\n",
       "Great Lakes, Midwestern US                                                              150\n",
       "Western US                                                                              149\n",
       "Greater San Diego Area, West Coast, Western US                                          136\n",
       "Washington DC Metro Area, East Coast, Southern US                                       124\n",
       "West Coast, Western US                                                                  121\n",
       "Greater Atlanta Area, East Coast, Southern US                                           109\n",
       "East Coast, Southern US                                                                  98\n",
       "East Coast, New England, Northeastern US                                                 81\n",
       "Greater Philadelphia Area, Great Lakes, Northeastern US                                  77\n",
       "Research Triangle, East Coast, Southern US                                               65\n",
       "Dallas/Fort Worth Metroplex, Southern US                                                 64\n",
       "Greater Miami Area, East Coast, Southern US                                              56\n",
       "Nordic Countries, Scandinavia                                                            54\n",
       "Greater Minneapolis-Saint Paul Area, Great Lakes, Midwestern US                          54\n",
       "Midwestern US                                                                            53\n",
       "Gulf Cooperation Council (GCC)                                                           48\n",
       "Washington DC Metro Area, Southern US                                                    47\n",
       "Greater Phoenix Area, Western US                                                         46\n",
       "Greater Baltimore-Maryland Area, East Coast, Southern US                                 44\n",
       "Great Lakes, Northeastern US                                                             38\n",
       "Other                                                                                    37\n",
       "Greater Houston Area, Southern US                                                        33\n",
       "Greater Detroit Area, Great Lakes, Midwestern US                                         31\n",
       "Tampa Bay Area, East Coast, Southern US                                                  24\n",
       "East Coast, Northeastern US                                                              21\n",
       "Name: Headquarters Regions, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMain[\"Headquarters Regions\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Commerce and Shopping               1310\n",
       "Apps                                 864\n",
       "Financial Services                   830\n",
       "Artificial Intelligence              697\n",
       "Data and Analytics                   660\n",
       "Information Technology               625\n",
       "Biotechnology                        605\n",
       "Administrative Services              559\n",
       "Consumer Electronics                 531\n",
       "Advertising                          520\n",
       "Health Care                          485\n",
       "Hardware                             443\n",
       "Education                            355\n",
       "Community and Lifestyle              329\n",
       "Content and Publishing               278\n",
       "Clothing and Apparel                 275\n",
       "Internet Services                    270\n",
       "Energy                               168\n",
       "Food and Beverage                    166\n",
       "Agriculture and Farming              137\n",
       "Gaming                               129\n",
       "Other                                103\n",
       "Media and Entertainment               87\n",
       "Events                                79\n",
       "Software                              76\n",
       "Design                                72\n",
       "Consumer Goods                        66\n",
       "Manufacturing                         62\n",
       "Real Estate                           58\n",
       "Mobile                                54\n",
       "Transportation                        50\n",
       "Professional Services                 40\n",
       "Government and Military               30\n",
       "Travel and Tourism                    22\n",
       "Science and Engineering               16\n",
       "Privacy and Security                  16\n",
       "Sales and Marketing                   16\n",
       "Natural Resources                      6\n",
       "Sports                                 5\n",
       "Navigation and Mapping                 2\n",
       "Messaging and Telecommunications       1\n",
       "Sustainability                         1\n",
       "Name: Industry, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMain[\"Industry\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\iqbal\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:671: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "#Remove Outliers\n",
    "dfMain = dfMain[dfMain[\"Estimated Revenue Range\"] != \"$10B+\"]\n",
    "\n",
    "\n",
    "#Initialize empty columns\n",
    "dfMain[\"Industry\"]=np.nan\n",
    "dfMain[\"Count\"]=np.nan\n",
    "\n",
    "# Get First value of Industry Group\n",
    "dfMain[\"Industry\"] = dfMain[\"Industry Groups\"].str.split(',')\n",
    "dfMain[\"Industry\"] = dfMain[\"Industry\"].apply(lambda x: x[0])\n",
    "\n",
    "# Create Years Founded Variable\n",
    "dfMain['Years Active'] = pd.to_datetime(dfMain[\"Founded Date\"], errors=\"coerce\")\n",
    "dfMain[\"Years Active\"] = pd.DatetimeIndex(dfMain[\"Years Active\"]).year\n",
    "dfMain[\"Years Active\"] = 2020  - dfMain[\"Years Active\"]\n",
    "dfMain[\"Years Active\"].fillna(10, inplace=True)\n",
    "\n",
    "\n",
    "#Set Categorical and numeric variables\n",
    "cat_vars = dfMain[[\"Headquarters Regions\", \"Industry\", \"Estimated Revenue Range\", \"Number of Employees\",]]\n",
    "\n",
    "num_vars = dfMain[['Number of Articles', 'Number of Founders', 'Number of Funding Rounds', 'Years Active',\n",
    "                   'Last Funding Amount Currency (in USD)',\n",
    "                   'Last Equity Funding Amount Currency (in USD)',\n",
    "                   'Total Equity Funding Amount Currency (in USD)',\n",
    "                   'Total Funding Amount Currency (in USD)', 'Number of Lead Investors',\n",
    "                   'Number of Investors', 'BuiltWith - Active Tech Count', \"SEMrush - Monthly Visits\",\n",
    "                   'SEMrush - Average Visits (6 months)', \"SEMrush - Visit Duration\", \"SEMrush - Page Views / Visit\"\n",
    "                    ]]\n",
    "\n",
    "\n",
    "#Apply One hot encode\n",
    "ohe = OneHotEncoder()\n",
    "transformed_cat_vars = ohe.fit_transform(cat_vars.astype(str))\n",
    "feat_names = ohe.get_feature_names()\n",
    "\n",
    "\n",
    "#Handle Headerquarters regions with low values\n",
    "dfMain[\"Count\"]=dfMain.groupby(\"Headquarters Regions\").transform('count')\n",
    "dfMain[\"Headquarters Regions\"].loc[dfMain[\"Count\"] < 20 ] = \"Other\"\n",
    "\n",
    "#Remove commas from numbers\n",
    "locale.setlocale(locale.LC_NUMERIC, '')\n",
    "num_vars = num_vars.astype(str)\n",
    "num_vars = num_vars.applymap(atof)\n",
    "\n",
    "\n",
    "\n",
    "#scale numerical variables\n",
    "scaled_num_vars = preprocessing.StandardScaler().fit_transform(num_vars)\n",
    "\n",
    "\n",
    "# transformed_vars = pd.concat([num_vars, transformed_cat_vars], axis=1)\n",
    "\n",
    "#Create Target Variable\n",
    "targetValues = [\"Series A\", \"Series B\", \"Series C\", \"Series D\", \"Series E\", \"Series F\", \n",
    "                \"Series G\",  \"Series H\", \"Series I\", \"Series J\", \"Post-IPO Equity\"]\n",
    "\n",
    "dfMain[\"Has Reached Series A\"]=dfMain[\"Last Equity Funding Type\"].isin(targetValues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.05756426,  1.52923397,  0.38800399, ..., -0.01855721,\n",
       "         0.22474861, -0.32802982],\n",
       "       [-0.02559964,  1.52923397,  0.38800399, ..., -0.0188742 ,\n",
       "        -0.06342542,  0.14717568],\n",
       "       [ 0.10998272, -0.18782781, -0.37738319, ..., -0.01527725,\n",
       "         0.32915949,  0.0383937 ],\n",
       "       ...,\n",
       "       [-0.04324046, -1.04635869, -0.76007677, ..., -0.01876694,\n",
       "         0.57452507, -0.17535335],\n",
       "       [-0.03895626,  0.67070308,  0.0053104 , ..., -0.01885173,\n",
       "        -0.25345323, -0.13336732],\n",
       "       [-0.04046833, -0.18782781, -0.76007677, ..., -0.01856182,\n",
       "         0.53798126, -0.27077614]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_num_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     6490\n",
       "False    4608\n",
       "Name: Has Reached Series A, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMain[\"Has Reached Series A\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Number of Articles                              -0.007323\n",
       "Number of Founders                               0.077417\n",
       "Number of Funding Rounds                         0.114631\n",
       "Years Active                                    -0.049818\n",
       "Last Funding Amount Currency (in USD)            0.048748\n",
       "Last Equity Funding Amount Currency (in USD)     0.032204\n",
       "Total Equity Funding Amount Currency (in USD)    0.019229\n",
       "Total Funding Amount Currency (in USD)           0.018138\n",
       "Number of Lead Investors                         0.215619\n",
       "Number of Investors                              0.222137\n",
       "BuiltWith - Active Tech Count                    0.081618\n",
       "SEMrush - Monthly Visits                         0.010303\n",
       "SEMrush - Average Visits (6 months)              0.010361\n",
       "SEMrush - Visit Duration                         0.035443\n",
       "SEMrush - Page Views / Visit                     0.026032\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_vars.corrwith(dfMain[\"Has Reached Series A\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22b1cbd5850>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(dfMain['Headquarters Regions'] ,dfMain[\"Has Reached Series A\"]).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22b1cf59af0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(dfMain['Estimated Revenue Range'] ,dfMain[\"Has Reached Series A\"]).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22b1cdad640>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(dfMain['Number of Employees'] ,dfMain[\"Has Reached Series A\"]).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x22b1ce3c8e0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Has Reached Series A\", y=\"Number of Founders\", kind=\"box\", data=dfMain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x22b68521c40>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9h8eCmP6eeuopAH7yk58kjsTqkSd9raxyasBfBk6PiEM8FsT01tvby9NPPw3A008/7dGqrKL6+/vp6uoiIujq6nItuALKScDPRsTjuUdiU+bRqixP7uZYeeUk4B5Jfyvp7Kw54szx3pKzNDxaleXJ3Rwrr5wEfDDwAnAKcFq2vCfPoGxyPFqV5cndHCtvzFeRpxO/ilye3t5ePvKRj7xS/sY3vuEBU6xi/Kr7lIz6KnI5syJfS+F15GE8K/L049GqLE9NTU0cd9xx3H///Rx33HFOvhVQ1pxw/NdMyPdQaJLYPd5Bkl4v6T5Jj0t6TNLKbPt8Sd1Z17ZuSfOm8g3YcKtWreLAAw/0aFWWi0ceeQSATZs2JY6kPoxbA46Im4vLkq6nMEX9eAaAT0fEQ5IOAh6U1A2cB9wTEZdKuhC4EPCsyxXS2trKHXfckToMq0M9PT288MILALzwwgs8+OCDHHfccYmjqm1lvQk3wlHAEePtFBHbIuKhbH0X8DiwEDgD6Mx26wTeO4kYzKzKLrroomHlL3zhC2kCqSPltAHvYngb8C+ZYI1VUgvw68CPgMOGRleLiG2SDh3jmPOB8wGOOGLcfG9mOdu9e3fJsk1cqRkxGiNiICIOmsoFJM0FbgY+GRHPS6M+DHyViFgLrIVCL4ipxGBmUzd37txhSXfu3LkJo6kPpZogHhhakXTVZE4uaT8Kyfe6oimMnpW0IPv6AmD7ZM5tZtU1sgni4osvThNIHSmVgIurqidO9MQqVHW/CTweEWuKvnQr0JGtdwC3TPTcZlZ9bW1tzJkzB4A5c+b4AVwFlGoDnuqf/ScCHwS2SBrqs/I54FLgRkkfBn4O/O4Ur2NmVTLUhFhuU6KVVioBv1nSZgo14SOzdbJyRMQxpU4cET9gjLc/gN+ecKRmllRPTw979+4FYO/eve6GVgGlZkV+Q6kDI+JnuUQ0Cr+KbJbee97znlc9hLv99tsTRlRTJvYqcjUTrJlNf+6GVnmTeRHDzGagkd3O3A1t6pyAzaws7oZWeWMmYEn3ZJ9fql44NlWeNNHy0tbWxuzZswGYPXu2H8BVQKka8AJJ7wBOl/TrkhYXL9UK0CbGkyZanopnxLCpK5WA/5TCSGWHA2uAy4qWr+Qfmk2UJ020PN17770MDg4ChTnh7rvvvsQR1b4xE3BE/F1EvAv4ckQsGbGcXMUYrUyeNNHy9MUvfnFY+ZJLLkkUSf0Y9yFcRPy5pNMlfSVbPB/cNOVJEy1PI5sd3AwxdeMmYEl/AawE/jlbVmbbbJrxpImWp6F7a6yyTVw53dBOBZZFxDURcQ3Qnm2zaaajo4NZswq/0oaGBpYvX544Iqsnn/vc54aVP//5zyeKpH6U2w/4tUXrh+QRiE3d0KSJAIsXL/akiVZRJ598Mg0NDUDhP/glS5Ykjqj2lfM3xF8AD0u6j8L7zG8HPptrVDZpmzdvHvZpVknz5s1j586dzJvnuXQroZyHcNcDbwO+ky0nRMQNeQdmE9fT08OePXsA2LNnDw8++GDiiKye9Pb2snPnTgB27txJb29v4ohq35ijoU0nHg2tPB6tyvJ03nnn0dfX90q5paWFdevWJYunxow6GprHgqgjHq3K8lScfEcr28Q5AdcRj1ZleWppaSlZtokrmYAlzZL0aLWCsanxaFWWp1WrVpUs28SVTMARsQ94RNIRVYrHpqCtrY39998f8GhVVnmtra3Mnz8fgPnz59Pa2po4otpXTje0BcBjkh4A9gxtjIjTc4vKJu2ll14C4MUXX0wcidWj5557btinTU05Cdh/x9aIW265ZVj5tttu47TTTksUjdUb31+VV1Y3tGyCzqMiYoOk1wANEbEr9+gy7oZWniVLllD8+5TkIQOtYnx/TcnkuqFJ+ijwd8DXs00Lge9VLi6rlJH/mdZCH2+rHb6/Kq+cbmgfB04EngeIiCeBQ/MMyiZHUsmy2VT4/qq8chLwixHx0lBBUiPg//qmoU9+8pPDyp/61KcSRWL1yPdX5ZWTgP9B0ueAAyQtA24Cbss3LJuMM844Y1jZD0isknx/VV45CfhCYAewBfgD4E5g3B7Ykq6RtL34RQ5JF0naKmlTtrx7soGbmdW6ckZD2wd0An9OoUtaZ5TX+r6OwuDtI301IhZly50TCdZKG62bkFmlXHfddcPKN9zgQRGnqpxeEKcCPwGuBL4G9Ep613jHRcT3AffWrqLLL798WHnNmjWJIrF6dPXVVw8r//Vf/3WiSOpHOU0QlwFLIuKkiHgHsAT46hSueYGkzVkTxZijOks6X1KPpJ4dO3ZM4XIzh7sJmdWWchLw9ogoHnn5KWD7JK/3V8CRwCJgG4XkPqqIWBsRbRHR1tzcPMnLzSzuJmRWW8ZMwJLOlHQmhXEg7pR0nqQOCj0gfjyZi0XEsxExmLUrXw0cP6mobVTuJmR5+uhHPzqs/LGPfSxRJPWjVA34tGyZAzwLvAM4iUKPiElNCCVpQVHxfYCHuqwgdxOyPJ1zzjnDyh/4wAcSRVI/xhyMJyI+NJUTS7qeQsJ+naRngC8AJ0laROFFjj4K3drMzGakcnpB/KqkNZK+I+nWoWW84yLi7IhYEBH7RcThEfHNiPhgRBwdEcdExOkRsa0y34bBqwdgv+SSSxJFYvVoxYoVw8pu4pq6ch7CfY9CbfUqCg/NhhabZkaOTNXd3Z0oEqtHmzdvHlZ+6KGHEkVSP8oZD/g/I+LK3CMxM5thyknAV0j6AnA38Mo0CxHh//7MzKagnAR8NPBB4GRgX7YtsrJNI0uWLBnWDLFs2bKE0Vi9OeaYY4Y1QyxevDhhNPVh3BkxJD0BHFM8JGW1eUaM8p100kmvrG/cuDFZHFaffH9N2uRmxAAeAV5b2VjMzKycBHwY8ISkuybSDc2q76yzzhpWfv/7358oEqtHZ5999rDyueeemyiS+lFOG/AXco/CKqK/v39Yefv2yQ7ZYfZq27YN77b/zDPPJIqkfoybgCPiH6oRiJnZTDNuApa0i/+aA25/YD9gT0QcnGdgZmb1rpwZMQ6KiIOzZQ5wFoWB2W2aaWpqGlY+9FBPXm2Vs2DBgmHlww8/PFEk9aOch3DDRMT3cB/gaenmm28eVr7xxhsTRWL16Prrrx9W/ta3vpUokvpRThPEmUXFWUAbnpbezGzKyqkBn1a0vBPYBZxR8ghLYsmSJcPKJ5/sP1SscpYuXTqs7Dctp66cXhBTGhfYqmfkW4379u0bY0+ziRsYGBhWfvnllxNFUj/GTMCS/rTEcRERf55DPGZmM0apGvCeUbYdCHwYaAKcgM3MpqDUlESvDLou6SBgJfAh4AY8IPu0JGlYM8SsWRPu5GI2psbGxmHNEPvtt1/CaOpDyX+hkuZLWg1sppCsF0fEn0SE33GdhkbOiHHvvfcmisTq0YYNG4aVPePK1JVqA/5L4ExgLXB0ROyuWlRmZjNAqRrwp4FfAVYBv5D0fLbskvR8dcKziTjllFOGld/5zncmisTqUfFYwKOVbeJKtQG7AbHGvPTS8DHzX3zxxTH2NLPpwEnWzCwRJ2Azs0ScgOvI/vvvP6w8e/bsRJGYWTmcgOvI3XffPax81113JYrE6tHISTg9KefU5ZaAJV0jabukR4u2zZfULenJ7HNeXtc3M5vu8qwBrwPaR2y7ELgnIo4C7snKViHuJmR58v1Vebkl4Ij4PvDciM1nAJ3Zeifw3ryub2Y23VW7DfiwiNgGkH2OOWeOpPMl9Ujq2bFjR9UCNDOrlmn7EC4i1kZEW0S0NTc3pw7HzKziqp2An5W0ACD79KA+ZjZjVTsB3wp0ZOsdwC1Vvn5dczchy5Pvr8rLsxva9cD9wH+X9IykDwOXAsskPQksy8pmZjOSRs4jNh21tbVFT09P6jDMZrTRup25Flw2jbZx3Ek5bXKuuuoqent7q37drVu3ArBw4cKqXxugtbWVT3ziE0mubVZrnIDrzN69e1OHYGZlcgLOSapa4MqVKwG44oorklzfzMo3bfsBm5nVOydgMyuLu6FVnhOwmVkibgM2s7K51ltZTsBmNcjdHOuDE7CZlc3dHCvLCdisBrmbY33wQzgzs0ScgM3MEnECNjNLxAnYzCwRJ2Azs0ScgM3MEnECNjNLxAnYzCwRJ2Azs0ScgM3MEnECNjNLxAnYzCwRJ2Azs0ScgM3MEnECNjNLxAnYzCyRJAOyS+oDdgGDwEBEtKWIw8wspZQzYiyJiJ0Jr29mlpQiovoXLdSA28pNwG1tbdHT0zPh66SauDCloe+3tbU1cSTVlWqyxpl2j/n+mjSNtjFVDTiAuyUF8PWIWDtyB0nnA+cDHHHEEZO6SG9vL5sefZzB18yfSqw1ZdZLhf9QH3zq2cSRVE/DC88lu3Zvby9PPvYwR8wdTBZDNe3/cuGx0Ys/m3iFqFb9fHdDbudOlYBPjIhfSDoU6Jb0RER8v3iHLCmvhUINeLIXGnzNfPa++d1Ti9amtQOeuDPp9Y+YO8jnFj+fNAbLzxcfOji3cyfpBRERv8g+twPfBY5PEYeZWUpVT8CSDpR00NA6cArwaLXjMDNLLUUTxGHAdyUNXf/bEdGVIA4zs6SqnoAj4ing2Gpf18xsuknZD9is5m3dupU9uxpyfVBjaf1sVwMHbt2ay7n9KrKZWSKuAZtNwcKFC3lxYJu7odWxLz50MLMXLszl3K4Bm5kl4gRsZpaIE7CZWSJOwGZmidT1Q7itW7fS8MJ/JB8rwPLV8EI/W7cOJLv+z3fPnG5oz75QqLMd9pp9iSOpnp/vbuConM5d1wnYLG8zbVjGl7LhKGe/YeZ830eR3++5rhPwwoUL+eWLjR4Nrc4d8MSdLFx4WJJrpxiDOKWVK1cCcMUVVySOpD64DdjMLBEnYDOzRJyAzcwScQI2M0ukrh/CQWG+sJnUDW3WfxbGJNg3Z2Z0i4KhOeHSPIQzm4q6TsAzrYsQQG/vLgBa3ziTEtJhM/J3bbWvrhPwTOsiBO4mZFZL3AZsZpaIE7CZWSJOwGZmiTgBm5kloohIHcO42traoqenJ3UYE3LVVVfRmw1cUk1D10zVK6C1tXVGPvysNt9fNUejbazrXhAz0QEHHJA6BKtjvr8qyzVgM7P8jVoDdhuwmVkiTsBmZokkScCS2iX9i6ReSRemiMHMLLWqJ2BJDcD/Bd4FvAU4W9Jbqh2HmVlqKWrAxwO9EfFURLwE3ACckSAOM7OkUiTghcDTReVnsm3DSDpfUo+knh07dlQtODOzakmRgEfrjvGqvnARsTYi2iKirbm5uQphmZlVV4oE/Azw+qLy4cAvEsRhZpZUigT8Y+AoSb8qaX/gA8CtCeIwM0uq6q8iR8SApAuAu4AG4JqIeKzacZiZpZZkLIiIuBOYORO1mZmNwm/CmZklUhOD8UjaAfwsdRw15HXAztRBWN3y/TVxOyOifeTGmkjANjGSeiKiLXUcVp98f1WOmyDMzBJxAjYzS8QJuD6tTR2A1TXfXxXiNmAzs0RcAzYzS8QJ2MwsEc+KXAMkDQJbija9NyL6xth3d0TMrUpgVlckNQH3ZMX/BgwCQ2PBHp+N320V5DbgGjCRpOoEbJUg6SJgd0R8pWhbY0QMpIuq/rgJogZJmivpHkkPSdoi6VUzikhaIOn7kjZJelTS/8q2nyLp/uzYmyQ5WduYJK2TtEbSfcCXJF0k6TNFX39UUku2fq6kB7J77uvZ9GNWghNwbTggu6k3Sfou8J/A+yJiMbAEuEzSyIHu/zdwV0QsAo4FNkl6HbAKWJod2wN8qnrfhtWoN1G4Zz491g6Sfg34PeDE7J4bBM6pUnw1y23AtWFvdlMDIGk/4IuS3g7sozCl02HAL4uO+TFwTbbv9yJik6R3UJgI9Z+yfL0/cH+VvgerXTdFxOA4+/w2cBzw4+zeOgDYnndgtc4JuDadAzQDx0XEy5L6gDnFO0TE97MEfSrwN5L+Evg3oDsizq52wFbT9hStDzD8L+eh+05AZ0R8tmpR1QE3QdSmQ4DtWfJdArxh5A6S3pDtczXwTWAx8EPgREmt2T6vkfSmKsZtta+Pwr2EpMXAr2bb7wF+R9Kh2dfmZ/egleAacG26DrhNUg+wCXhilH1OAv5I0svAbg/3kIUAAAQDSURBVGB5ROyQdB5wvaTZ2X6rgH/NP2SrEzcDyyVtotDM9a8AEfHPklYBd0uaBbwMfBwPI1uSu6GZmSXiJggzs0ScgM3MEnECNjNLxAnYzCwRJ2Azs0ScgG1CJO0eUT5P0tcqcN7BonErbpP02qmec8T5d4+/V8nj+7JXuUdu//1sPI7NWeyvGpdjnPOeLunCqcRWdK73SQpJb67E+Sx/TsA2XeyNiEUR8VbgOQp9SKc1SYcDnwd+KyKOAd4GbJ7A8Y0RcWtEXFqhkM4GfgB8oELns5w5AVvFSDpN0o8kPSxpg6TDsu3vKBpM6GFJB41zqvspjG+BpCMldUl6UNI/DtXuSlxrrqRri2qlZxXFd4mkRyT9sGj/Zkk3S/pxtpyYbW+SdHd2/q9TeNV2pEOBXRRedCEidkfET8eJe+ToYq/8BVEilnF/ftmodicCH8YJuHZEhBcvZS8URrnaVLT8HPha9rV5/NfLPR8BLsvWb6MwShbAXKBxlPPuzj4bgJuA9qx8D3BUtv4bwL3jXOtLwOVF552XfQZwWrb+ZWBVtv5tCjVYgCOAx7P1K4E/zdZPzY5/3YiYG4C7sp/BtUPnHyfudcDtQENWPq/o5zdWLOX8/M4Fvpmt/z9gcep7xcv4i19FtokaOTLbeUBbVjwc+FtJCyiMtPbTbPs/AWskXQd8JyKeGeW8B2Svt7YADwLdWa3uN4GbikbbHHqFeqxrLaWoBhgR/5atvkQh8ZGdf1nR/m8pOv/BWQ3z7cCZ2TnukDR0nldExKCkduB/UhgN7KuSjgO+UiJuGHt0sbFiKefndzZwebZ+Q1Z+aJT9bBpxArZKugpYExG3SjoJuAggIi6VdAfwbuCHkpZGxMjxK/ZGxCJJh1BIlB+nUFv89+KEP961KDQVjPZ+/cuRVQ8p1OKH7v1ZwAkRsbd45ywJjvuefnbOB4AHJHVTqAmvKRE3DB9drNiosQAlf34qTCV0MvBWSUGhZh6S/rjoe7ZpyG3AVkmHAFuz9Y6hjZKOjIgtEfElCoPAj/mUPiL+A1gBfAbYC/xU0u9m55GkY0tdC7gbuKDo2vPGiXnk/kNJ8/tkA4pLeheFJo9hJP2KCiOCDVkE/Cwini8R94RjKePn9zvA+oh4Q0S0RMTrKfxF8FtlXNMScgK2SrqIwp/d/wjsLNr+yayL1iMUkurflzpJRDwMPEKhKeEc4MPZsY8BQ928xrrWamBe0fWWjBPzCqAte2D3z8DHsu0XA2+X9BBwCoV23pH2A74i6Yms+eT3gJXZ18aKezKxjPfzOxv47ohtN1OYFcWmMY+GZmaWiGvAZmaJOAGbmSXiBGxmlogTsJlZIk7AZmaJOAGbmSXiBGxmlsj/B5aRevoFA2kkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Has Reached Series A\", y=\"Number of Funding Rounds\", kind=\"box\", data=dfMain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x22b1cd667c0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Has Reached Series A\", y=\"Number of Lead Investors\", kind=\"box\", data=dfMain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = transformed_cat_vars\n",
    "x2 = scaled_num_vars\n",
    "\n",
    "y = dfMain[\"Has Reached Series A\"]\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y)\n",
    "x2_train, x2_test, y2_train, y2_test = train_test_split(x2, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['x0_Asia-Pacific (APAC)' 0.02298131272442581]\n",
      " ['x0_Asia-Pacific (APAC), Association of Southeast Asian Nations (ASEAN), Southeast Asia'\n",
      "  0.011859077047154509]\n",
      " ['x0_Asia-Pacific (APAC), Australasia' 0.011145268186472018]\n",
      " ['x0_Central America, Latin America' 6.750043047885874e-05]\n",
      " ['x0_Dallas/Fort Worth Metroplex, Southern US' 0.004341559424254767]\n",
      " ['x0_East Coast, New England, Northeastern US' 0.0053761747530351196]\n",
      " ['x0_East Coast, Northeastern US' 0.0019743817318976713]\n",
      " ['x0_East Coast, Southern US' 0.006688687843782798]\n",
      " ['x0_European Union (EU)' 0.023790633417929394]\n",
      " ['x0_European Union (EU), Nordic Countries, Scandinavia'\n",
      "  0.013968104426754528]\n",
      " ['x0_Great Lakes' 0.009808511146453503]\n",
      " ['x0_Great Lakes, East Coast, Northeastern US' 0.001786024795334694]\n",
      " ['x0_Great Lakes, Midwestern US' 0.009064235349246509]\n",
      " ['x0_Great Lakes, Northeastern US' 0.003621306540062663]\n",
      " ['x0_Greater Atlanta Area, East Coast, Southern US' 0.00749422978690688]\n",
      " ['x0_Greater Baltimore-Maryland Area, East Coast, Southern US'\n",
      "  0.0042486570460133445]\n",
      " ['x0_Greater Boston Area, East Coast, New England' 0.01627931332311134]\n",
      " ['x0_Greater Chicago Area, Great Lakes, Midwestern US'\n",
      "  0.010105668792136561]\n",
      " ['x0_Greater Denver Area, Western US' 0.00856899738869858]\n",
      " ['x0_Greater Detroit Area, Great Lakes, Midwestern US'\n",
      "  0.0033442315963789853]\n",
      " ['x0_Greater Houston Area, Southern US' 0.0034715154819169552]\n",
      " ['x0_Greater Los Angeles Area, Inland Empire, West Coast'\n",
      "  0.00012875334456060095]\n",
      " ['x0_Greater Los Angeles Area, West Coast, Western US'\n",
      "  0.01723964792058139]\n",
      " ['x0_Greater Miami Area, East Coast, Southern US' 0.004841800630815347]\n",
      " ['x0_Greater Minneapolis-Saint Paul Area, Great Lakes, Midwestern US'\n",
      "  0.004078080342035257]\n",
      " ['x0_Greater New York Area, East Coast, Northeastern US'\n",
      "  0.016911163894745798]\n",
      " ['x0_Greater Philadelphia Area, East Coast, Northeastern US'\n",
      "  0.00010610932810713274]\n",
      " ['x0_Greater Philadelphia Area, East Coast, Southern US'\n",
      "  0.001048464774702389]\n",
      " ['x0_Greater Philadelphia Area, Great Lakes, Northeastern US'\n",
      "  0.005249498748828215]\n",
      " ['x0_Greater Phoenix Area, Western US' 0.0032426230210718895]\n",
      " ['x0_Greater San Diego Area, West Coast, Western US'\n",
      "  0.006849284332231592]\n",
      " ['x0_Greater Seattle Area, West Coast, Western US' 0.012463777579424828]\n",
      " ['x0_Gulf Cooperation Council (GCC)' 0.0031430027260335486]\n",
      " ['x0_Latin America' 0.010002930426244063]\n",
      " ['x0_Midwestern US' 0.003865971080735783]\n",
      " ['x0_New England, Northeastern US' 0.000680710273312825]\n",
      " ['x0_Nordic Countries, Scandinavia' 0.003559853619762299]\n",
      " ['x0_Research Triangle, East Coast, Southern US' 0.004903285685996088]\n",
      " ['x0_San Francisco Bay Area, Silicon Valley, West Coast'\n",
      "  0.018721854217514506]\n",
      " ['x0_San Francisco Bay Area, West Coast, Western US'\n",
      "  0.020712260038864217]\n",
      " ['x0_Southern US' 0.01153354575878212]\n",
      " ['x0_Tampa Bay Area, East Coast, Southern US' 0.0031293461940299807]\n",
      " ['x0_Washington DC Metro Area, East Coast, Southern US'\n",
      "  0.008692364695010125]\n",
      " ['x0_Washington DC Metro Area, Southern US' 0.002806375943011382]\n",
      " ['x0_West Coast, Western US' 0.007560448302511895]\n",
      " ['x0_Western US' 0.008983947228593994]\n",
      " ['x1_Administrative Services' 0.017260743130196217]\n",
      " ['x1_Advertising' 0.016173322673989066]\n",
      " ['x1_Agriculture and Farming' 0.007829180617129686]\n",
      " ['x1_Apps' 0.018731608853984272]\n",
      " ['x1_Artificial Intelligence' 0.013644112747687577]\n",
      " ['x1_Biotechnology' 0.017973152149232577]\n",
      " ['x1_Clothing and Apparel' 0.01137265313263173]\n",
      " ['x1_Commerce and Shopping' 0.02496534788965157]\n",
      " ['x1_Community and Lifestyle' 0.01364938182881076]\n",
      " ['x1_Consumer Electronics' 0.018100596982370314]\n",
      " ['x1_Consumer Goods' 0.006123970353189587]\n",
      " ['x1_Content and Publishing' 0.014271776364178316]\n",
      " ['x1_Data and Analytics' 0.019151906175459914]\n",
      " ['x1_Design' 0.006162484176008352]\n",
      " ['x1_Education' 0.014246937206449858]\n",
      " ['x1_Energy' 0.009877251272025576]\n",
      " ['x1_Events' 0.006252761682036079]\n",
      " ['x1_Financial Services' 0.02165293677531109]\n",
      " ['x1_Food and Beverage' 0.009175358084157223]\n",
      " ['x1_Gaming' 0.008965113956346594]\n",
      " ['x1_Government and Military' 0.0017490902410327183]\n",
      " ['x1_Hardware' 0.015458661791870927]\n",
      " ['x1_Health Care' 0.019370397932849667]\n",
      " ['x1_Information Technology' 0.01960488216971577]\n",
      " ['x1_Internet Services' 0.014940134686190317]\n",
      " ['x1_Manufacturing' 0.005030369315893425]\n",
      " ['x1_Media and Entertainment' 0.007294989676214958]\n",
      " ['x1_Messaging and Telecommunications' 4.900808061763236e-05]\n",
      " ['x1_Mobile' 0.003593017187045844]\n",
      " ['x1_Natural Resources' 0.000850636314874225]\n",
      " ['x1_Navigation and Mapping' 0.00019112595179917642]\n",
      " ['x1_Other' 0.006464796697707627]\n",
      " ['x1_Privacy and Security' 0.001346819225193999]\n",
      " ['x1_Professional Services' 0.003562838471981661]\n",
      " ['x1_Real Estate' 0.00429502619815343]\n",
      " ['x1_Sales and Marketing' 0.002101715788932592]\n",
      " ['x1_Science and Engineering' 0.0013047558389884398]\n",
      " ['x1_Software' 0.004901000408642842]\n",
      " ['x1_Sports' 0.000290487665886521]\n",
      " ['x1_Sustainability' 0.0]\n",
      " ['x1_Transportation' 0.0036958363824947677]\n",
      " ['x1_Travel and Tourism' 0.0025864362524230344]\n",
      " ['x2_$100M to $500M' 0.011902538481649764]\n",
      " ['x2_$10M to $50M' 0.022454864206246827]\n",
      " ['x2_$1B to $10B' 0.004630124959152426]\n",
      " ['x2_$1M to $10M' 0.023951079985688344]\n",
      " ['x2_$500M to $1B' 0.004573222435812749]\n",
      " ['x2_$50M to $100M' 0.014123198280991947]\n",
      " ['x2_Less than $1M' 0.021503593220395356]\n",
      " ['x3_1-10' 0.043337495935628564]\n",
      " ['x3_10001+' 0.003937234693811579]\n",
      " ['x3_1001-5000' 0.00827260519233555]\n",
      " ['x3_101-250' 0.024326120136041653]\n",
      " ['x3_11-50' 0.01879282079654209]\n",
      " ['x3_251-500' 0.012530851070602292]\n",
      " ['x3_5001-10000' 0.003160258414009202]\n",
      " ['x3_501-1000' 0.010740649516498672]\n",
      " ['x3_51-100' 0.01706022900528443]]\n",
      "                                              Features  Importances\n",
      "85                                   x1_Sustainability     0.000000\n",
      "73                 x1_Messaging and Telecommunications     0.000049\n",
      "3                    x0_Central America, Latin America     0.000068\n",
      "26   x0_Greater Philadelphia Area, East Coast, Nort...     0.000106\n",
      "21   x0_Greater Los Angeles Area, Inland Empire, We...     0.000129\n",
      "76                           x1_Navigation and Mapping     0.000191\n",
      "84                                           x1_Sports     0.000290\n",
      "35                     x0_New England, Northeastern US     0.000681\n",
      "75                                x1_Natural Resources     0.000851\n",
      "27   x0_Greater Philadelphia Area, East Coast, Sout...     0.001048\n",
      "82                          x1_Science and Engineering     0.001305\n",
      "78                             x1_Privacy and Security     0.001347\n",
      "66                          x1_Government and Military     0.001749\n",
      "11         x0_Great Lakes, East Coast, Northeastern US     0.001786\n",
      "6                       x0_East Coast, Northeastern US     0.001974\n",
      "81                              x1_Sales and Marketing     0.002102\n",
      "87                               x1_Travel and Tourism     0.002586\n",
      "43            x0_Washington DC Metro Area, Southern US     0.002806\n",
      "41          x0_Tampa Bay Area, East Coast, Southern US     0.003129\n",
      "32                   x0_Gulf Cooperation Council (GCC)     0.003143\n",
      "101                                      x3_5001-10000     0.003160\n",
      "29                 x0_Greater Phoenix Area, Western US     0.003243\n",
      "19   x0_Greater Detroit Area, Great Lakes, Midweste...     0.003344\n",
      "20                x0_Greater Houston Area, Southern US     0.003472\n",
      "36                    x0_Nordic Countries, Scandinavia     0.003560\n",
      "79                            x1_Professional Services     0.003563\n",
      "74                                           x1_Mobile     0.003593\n",
      "13                     x0_Great Lakes, Northeastern US     0.003621\n",
      "86                                   x1_Transportation     0.003696\n",
      "34                                    x0_Midwestern US     0.003866\n",
      "96                                           x3_10001+     0.003937\n",
      "24   x0_Greater Minneapolis-Saint Paul Area, Great ...     0.004078\n",
      "15   x0_Greater Baltimore-Maryland Area, East Coast...     0.004249\n",
      "80                                      x1_Real Estate     0.004295\n",
      "4          x0_Dallas/Fort Worth Metroplex, Southern US     0.004342\n",
      "92                                     x2_$500M to $1B     0.004573\n",
      "90                                      x2_$1B to $10B     0.004630\n",
      "23      x0_Greater Miami Area, East Coast, Southern US     0.004842\n",
      "83                                         x1_Software     0.004901\n",
      "37       x0_Research Triangle, East Coast, Southern US     0.004903\n",
      "71                                    x1_Manufacturing     0.005030\n",
      "28   x0_Greater Philadelphia Area, Great Lakes, Nor...     0.005249\n",
      "5          x0_East Coast, New England, Northeastern US     0.005376\n",
      "56                                   x1_Consumer Goods     0.006124\n",
      "59                                           x1_Design     0.006162\n",
      "62                                           x1_Events     0.006253\n",
      "77                                            x1_Other     0.006465\n",
      "7                           x0_East Coast, Southern US     0.006689\n",
      "30   x0_Greater San Diego Area, West Coast, Western US     0.006849\n",
      "72                          x1_Media and Entertainment     0.007295\n",
      "14    x0_Greater Atlanta Area, East Coast, Southern US     0.007494\n",
      "44                           x0_West Coast, Western US     0.007560\n",
      "48                          x1_Agriculture and Farming     0.007829\n",
      "97                                        x3_1001-5000     0.008273\n",
      "18                  x0_Greater Denver Area, Western US     0.008569\n",
      "42   x0_Washington DC Metro Area, East Coast, South...     0.008692\n",
      "65                                           x1_Gaming     0.008965\n",
      "45                                       x0_Western US     0.008984\n",
      "12                       x0_Great Lakes, Midwestern US     0.009064\n",
      "64                                x1_Food and Beverage     0.009175\n",
      "10                                      x0_Great Lakes     0.009809\n",
      "61                                           x1_Energy     0.009877\n",
      "33                                    x0_Latin America     0.010003\n",
      "17   x0_Greater Chicago Area, Great Lakes, Midweste...     0.010106\n",
      "102                                        x3_501-1000     0.010741\n",
      "2                  x0_Asia-Pacific (APAC), Australasia     0.011145\n",
      "52                             x1_Clothing and Apparel     0.011373\n",
      "40                                      x0_Southern US     0.011534\n",
      "1    x0_Asia-Pacific (APAC), Association of Southea...     0.011859\n",
      "88                                   x2_$100M to $500M     0.011903\n",
      "31     x0_Greater Seattle Area, West Coast, Western US     0.012464\n",
      "100                                         x3_251-500     0.012531\n",
      "50                          x1_Artificial Intelligence     0.013644\n",
      "54                          x1_Community and Lifestyle     0.013649\n",
      "9    x0_European Union (EU), Nordic Countries, Scan...     0.013968\n",
      "93                                    x2_$50M to $100M     0.014123\n",
      "60                                        x1_Education     0.014247\n",
      "57                           x1_Content and Publishing     0.014272\n",
      "70                                x1_Internet Services     0.014940\n",
      "67                                         x1_Hardware     0.015459\n",
      "47                                      x1_Advertising     0.016173\n",
      "16     x0_Greater Boston Area, East Coast, New England     0.016279\n",
      "25   x0_Greater New York Area, East Coast, Northeas...     0.016911\n",
      "103                                          x3_51-100     0.017060\n",
      "22   x0_Greater Los Angeles Area, West Coast, Weste...     0.017240\n",
      "46                          x1_Administrative Services     0.017261\n",
      "51                                    x1_Biotechnology     0.017973\n",
      "55                             x1_Consumer Electronics     0.018101\n",
      "38   x0_San Francisco Bay Area, Silicon Valley, Wes...     0.018722\n",
      "49                                             x1_Apps     0.018732\n",
      "99                                            x3_11-50     0.018793\n",
      "58                               x1_Data and Analytics     0.019152\n",
      "68                                      x1_Health Care     0.019370\n",
      "69                           x1_Information Technology     0.019605\n",
      "39   x0_San Francisco Bay Area, West Coast, Western US     0.020712\n",
      "94                                    x2_Less than $1M     0.021504\n",
      "63                               x1_Financial Services     0.021653\n",
      "89                                     x2_$10M to $50M     0.022455\n",
      "0                               x0_Asia-Pacific (APAC)     0.022981\n",
      "8                               x0_European Union (EU)     0.023791\n",
      "91                                      x2_$1M to $10M     0.023951\n",
      "98                                          x3_101-250     0.024326\n",
      "53                            x1_Commerce and Shopping     0.024965\n",
      "95                                             x3_1-10     0.043337\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier().fit(x_train, y_train)\n",
    "\n",
    "print(np.c_[feat_names, clf.feature_importances_])\n",
    "print(pd.DataFrame({\"Features\":feat_names, \"Importances\":  clf.feature_importances_}).sort_values(by='Importances') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                         Features  Importances\n",
      "1                              Number of Founders     0.023197\n",
      "8                        Number of Lead Investors     0.032742\n",
      "2                        Number of Funding Rounds     0.038956\n",
      "13                       SEMrush - Visit Duration     0.052341\n",
      "14                   SEMrush - Page Views / Visit     0.052704\n",
      "9                             Number of Investors     0.055822\n",
      "11                       SEMrush - Monthly Visits     0.057206\n",
      "0                              Number of Articles     0.057652\n",
      "12            SEMrush - Average Visits (6 months)     0.058618\n",
      "10                  BuiltWith - Active Tech Count     0.059326\n",
      "3                                    Years Active     0.076563\n",
      "6   Total Equity Funding Amount Currency (in USD)     0.090206\n",
      "7          Total Funding Amount Currency (in USD)     0.100873\n",
      "4           Last Funding Amount Currency (in USD)     0.103505\n",
      "5    Last Equity Funding Amount Currency (in USD)     0.140290\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier().fit(x2_train, y2_train)\n",
    "print(pd.DataFrame({\"Features\":num_vars.columns, \"Importances\":  clf.feature_importances_}).sort_values(by='Importances') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6396396396396397\n"
     ]
    }
   ],
   "source": [
    "df5 = pd.DataFrame({\"Features\": feat_names, \"Coeff\": np.exp(clf.coef_[0]) }) \n",
    "prediction = clf.predict(x_test)\n",
    "print(accuracy_score(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>x3_1-10</td>\n",
       "      <td>0.297347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>x0_European Union (EU), Nordic Countries, Scan...</td>\n",
       "      <td>0.473507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>x0_East Coast, Southern US</td>\n",
       "      <td>0.475742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>x1_Navigation and Mapping</td>\n",
       "      <td>0.503272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>x0_Great Lakes, East Coast, Northeastern US</td>\n",
       "      <td>0.541936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>x0_Great Lakes, Northeastern US</td>\n",
       "      <td>0.542552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>x0_Tampa Bay Area, East Coast, Southern US</td>\n",
       "      <td>0.577972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>x1_Consumer Goods</td>\n",
       "      <td>0.593272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>x0_Nordic Countries, Scandinavia</td>\n",
       "      <td>0.596598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>x0_Great Lakes, Midwestern US</td>\n",
       "      <td>0.610135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>x1_Professional Services</td>\n",
       "      <td>0.625113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>x0_Dallas/Fort Worth Metroplex, Southern US</td>\n",
       "      <td>0.641178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>x0_Greater Miami Area, East Coast, Southern US</td>\n",
       "      <td>0.684995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>x1_Sales and Marketing</td>\n",
       "      <td>0.703356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>x1_Software</td>\n",
       "      <td>0.749998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>x0_New England, Northeastern US</td>\n",
       "      <td>0.760763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>x1_Energy</td>\n",
       "      <td>0.761800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>x1_Travel and Tourism</td>\n",
       "      <td>0.771817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>x1_Sustainability</td>\n",
       "      <td>0.774717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>x1_Privacy and Security</td>\n",
       "      <td>0.785285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>x3_11-50</td>\n",
       "      <td>0.797009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>x3_5001-10000</td>\n",
       "      <td>0.797172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>x1_Media and Entertainment</td>\n",
       "      <td>0.814655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>x0_Latin America</td>\n",
       "      <td>0.833618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>x2_$50M to $100M</td>\n",
       "      <td>0.844482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>x1_Internet Services</td>\n",
       "      <td>0.857690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>x1_Messaging and Telecommunications</td>\n",
       "      <td>0.858168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>x2_Less than $1M</td>\n",
       "      <td>0.873711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>x1_Clothing and Apparel</td>\n",
       "      <td>0.874267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>x1_Content and Publishing</td>\n",
       "      <td>0.877904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>x1_Real Estate</td>\n",
       "      <td>0.887504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>x0_Greater Philadelphia Area, Great Lakes, Nor...</td>\n",
       "      <td>0.888800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>x0_Greater Atlanta Area, East Coast, Southern US</td>\n",
       "      <td>0.890854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>x1_Events</td>\n",
       "      <td>0.900221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>x0_Greater Houston Area, Southern US</td>\n",
       "      <td>0.907155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>x0_Great Lakes</td>\n",
       "      <td>0.928283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>x1_Manufacturing</td>\n",
       "      <td>0.945110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>x2_$10M to $50M</td>\n",
       "      <td>0.955430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>x1_Health Care</td>\n",
       "      <td>0.962464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>x1_Mobile</td>\n",
       "      <td>0.965546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>x1_Sports</td>\n",
       "      <td>0.971657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>x0_Washington DC Metro Area, East Coast, South...</td>\n",
       "      <td>0.977153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>x1_Information Technology</td>\n",
       "      <td>0.978217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>x2_$100M to $500M</td>\n",
       "      <td>0.986813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>x0_Greater San Diego Area, West Coast, Western US</td>\n",
       "      <td>0.987528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>x0_Midwestern US</td>\n",
       "      <td>0.989709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>x1_Hardware</td>\n",
       "      <td>0.991342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>x0_European Union (EU)</td>\n",
       "      <td>0.995499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>x0_Greater Los Angeles Area, Inland Empire, We...</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>x1_Commerce and Shopping</td>\n",
       "      <td>1.002014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>x0_Greater Denver Area, Western US</td>\n",
       "      <td>1.003151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>x0_Greater Los Angeles Area, West Coast, Weste...</td>\n",
       "      <td>1.010238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>x1_Food and Beverage</td>\n",
       "      <td>1.020099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>x3_10001+</td>\n",
       "      <td>1.028540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>x0_Greater Baltimore-Maryland Area, East Coast...</td>\n",
       "      <td>1.033249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>x0_Research Triangle, East Coast, Southern US</td>\n",
       "      <td>1.037407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>x0_Greater Philadelphia Area, East Coast, Sout...</td>\n",
       "      <td>1.038705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>x1_Gaming</td>\n",
       "      <td>1.047907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>x0_Greater Phoenix Area, Western US</td>\n",
       "      <td>1.048052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>x2_$500M to $1B</td>\n",
       "      <td>1.052221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>x1_Education</td>\n",
       "      <td>1.059668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>x0_Gulf Cooperation Council (GCC)</td>\n",
       "      <td>1.068564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>x1_Transportation</td>\n",
       "      <td>1.073754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>x3_501-1000</td>\n",
       "      <td>1.081840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>x0_Western US</td>\n",
       "      <td>1.083231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>x1_Other</td>\n",
       "      <td>1.088564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>x0_Asia-Pacific (APAC)</td>\n",
       "      <td>1.096341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>x1_Financial Services</td>\n",
       "      <td>1.098197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>x0_Asia-Pacific (APAC), Australasia</td>\n",
       "      <td>1.107731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>x3_1001-5000</td>\n",
       "      <td>1.108882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>x2_$1B to $10B</td>\n",
       "      <td>1.115559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>x1_Community and Lifestyle</td>\n",
       "      <td>1.119618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>x0_Greater Seattle Area, West Coast, Western US</td>\n",
       "      <td>1.121387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>x0_East Coast, Northeastern US</td>\n",
       "      <td>1.172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>x0_Greater Minneapolis-Saint Paul Area, Great ...</td>\n",
       "      <td>1.200725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>x1_Administrative Services</td>\n",
       "      <td>1.204606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>x1_Consumer Electronics</td>\n",
       "      <td>1.209377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>x2_$1M to $10M</td>\n",
       "      <td>1.218646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>x1_Agriculture and Farming</td>\n",
       "      <td>1.223306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>x1_Advertising</td>\n",
       "      <td>1.242902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>x1_Natural Resources</td>\n",
       "      <td>1.246919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>x0_Greater Boston Area, East Coast, New England</td>\n",
       "      <td>1.255308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>x0_Southern US</td>\n",
       "      <td>1.276127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x0_Asia-Pacific (APAC), Association of Southea...</td>\n",
       "      <td>1.306695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>x1_Data and Analytics</td>\n",
       "      <td>1.318850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>x1_Apps</td>\n",
       "      <td>1.348741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>x0_East Coast, New England, Northeastern US</td>\n",
       "      <td>1.353684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>x0_Central America, Latin America</td>\n",
       "      <td>1.386521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>x0_West Coast, Western US</td>\n",
       "      <td>1.395299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>x0_Greater Philadelphia Area, East Coast, Nort...</td>\n",
       "      <td>1.416959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>x0_Greater Detroit Area, Great Lakes, Midweste...</td>\n",
       "      <td>1.427189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>x0_Greater Chicago Area, Great Lakes, Midweste...</td>\n",
       "      <td>1.451123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>x1_Artificial Intelligence</td>\n",
       "      <td>1.476063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>x3_51-100</td>\n",
       "      <td>1.479495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>x0_Greater New York Area, East Coast, Northeas...</td>\n",
       "      <td>1.549481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>x3_251-500</td>\n",
       "      <td>1.599569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>x1_Science and Engineering</td>\n",
       "      <td>1.618483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>x1_Biotechnology</td>\n",
       "      <td>1.628098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>x1_Government and Military</td>\n",
       "      <td>1.643777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>x0_Washington DC Metro Area, Southern US</td>\n",
       "      <td>1.648373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>x3_101-250</td>\n",
       "      <td>1.803869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>x1_Design</td>\n",
       "      <td>1.812172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>x0_San Francisco Bay Area, Silicon Valley, Wes...</td>\n",
       "      <td>1.813870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>x0_San Francisco Bay Area, West Coast, Western US</td>\n",
       "      <td>2.068642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Features     Coeff\n",
       "95                                             x3_1-10  0.297347\n",
       "9    x0_European Union (EU), Nordic Countries, Scan...  0.473507\n",
       "7                           x0_East Coast, Southern US  0.475742\n",
       "76                           x1_Navigation and Mapping  0.503272\n",
       "11         x0_Great Lakes, East Coast, Northeastern US  0.541936\n",
       "13                     x0_Great Lakes, Northeastern US  0.542552\n",
       "41          x0_Tampa Bay Area, East Coast, Southern US  0.577972\n",
       "56                                   x1_Consumer Goods  0.593272\n",
       "36                    x0_Nordic Countries, Scandinavia  0.596598\n",
       "12                       x0_Great Lakes, Midwestern US  0.610135\n",
       "79                            x1_Professional Services  0.625113\n",
       "4          x0_Dallas/Fort Worth Metroplex, Southern US  0.641178\n",
       "23      x0_Greater Miami Area, East Coast, Southern US  0.684995\n",
       "81                              x1_Sales and Marketing  0.703356\n",
       "83                                         x1_Software  0.749998\n",
       "35                     x0_New England, Northeastern US  0.760763\n",
       "61                                           x1_Energy  0.761800\n",
       "87                               x1_Travel and Tourism  0.771817\n",
       "85                                   x1_Sustainability  0.774717\n",
       "78                             x1_Privacy and Security  0.785285\n",
       "99                                            x3_11-50  0.797009\n",
       "101                                      x3_5001-10000  0.797172\n",
       "72                          x1_Media and Entertainment  0.814655\n",
       "33                                    x0_Latin America  0.833618\n",
       "93                                    x2_$50M to $100M  0.844482\n",
       "70                                x1_Internet Services  0.857690\n",
       "73                 x1_Messaging and Telecommunications  0.858168\n",
       "94                                    x2_Less than $1M  0.873711\n",
       "52                             x1_Clothing and Apparel  0.874267\n",
       "57                           x1_Content and Publishing  0.877904\n",
       "80                                      x1_Real Estate  0.887504\n",
       "28   x0_Greater Philadelphia Area, Great Lakes, Nor...  0.888800\n",
       "14    x0_Greater Atlanta Area, East Coast, Southern US  0.890854\n",
       "62                                           x1_Events  0.900221\n",
       "20                x0_Greater Houston Area, Southern US  0.907155\n",
       "10                                      x0_Great Lakes  0.928283\n",
       "71                                    x1_Manufacturing  0.945110\n",
       "89                                     x2_$10M to $50M  0.955430\n",
       "68                                      x1_Health Care  0.962464\n",
       "74                                           x1_Mobile  0.965546\n",
       "84                                           x1_Sports  0.971657\n",
       "42   x0_Washington DC Metro Area, East Coast, South...  0.977153\n",
       "69                           x1_Information Technology  0.978217\n",
       "88                                   x2_$100M to $500M  0.986813\n",
       "30   x0_Greater San Diego Area, West Coast, Western US  0.987528\n",
       "34                                    x0_Midwestern US  0.989709\n",
       "67                                         x1_Hardware  0.991342\n",
       "8                               x0_European Union (EU)  0.995499\n",
       "21   x0_Greater Los Angeles Area, Inland Empire, We...  1.000000\n",
       "53                            x1_Commerce and Shopping  1.002014\n",
       "18                  x0_Greater Denver Area, Western US  1.003151\n",
       "22   x0_Greater Los Angeles Area, West Coast, Weste...  1.010238\n",
       "64                                x1_Food and Beverage  1.020099\n",
       "96                                           x3_10001+  1.028540\n",
       "15   x0_Greater Baltimore-Maryland Area, East Coast...  1.033249\n",
       "37       x0_Research Triangle, East Coast, Southern US  1.037407\n",
       "27   x0_Greater Philadelphia Area, East Coast, Sout...  1.038705\n",
       "65                                           x1_Gaming  1.047907\n",
       "29                 x0_Greater Phoenix Area, Western US  1.048052\n",
       "92                                     x2_$500M to $1B  1.052221\n",
       "60                                        x1_Education  1.059668\n",
       "32                   x0_Gulf Cooperation Council (GCC)  1.068564\n",
       "86                                   x1_Transportation  1.073754\n",
       "102                                        x3_501-1000  1.081840\n",
       "45                                       x0_Western US  1.083231\n",
       "77                                            x1_Other  1.088564\n",
       "0                               x0_Asia-Pacific (APAC)  1.096341\n",
       "63                               x1_Financial Services  1.098197\n",
       "2                  x0_Asia-Pacific (APAC), Australasia  1.107731\n",
       "97                                        x3_1001-5000  1.108882\n",
       "90                                      x2_$1B to $10B  1.115559\n",
       "54                          x1_Community and Lifestyle  1.119618\n",
       "31     x0_Greater Seattle Area, West Coast, Western US  1.121387\n",
       "6                       x0_East Coast, Northeastern US  1.172000\n",
       "24   x0_Greater Minneapolis-Saint Paul Area, Great ...  1.200725\n",
       "46                          x1_Administrative Services  1.204606\n",
       "55                             x1_Consumer Electronics  1.209377\n",
       "91                                      x2_$1M to $10M  1.218646\n",
       "48                          x1_Agriculture and Farming  1.223306\n",
       "47                                      x1_Advertising  1.242902\n",
       "75                                x1_Natural Resources  1.246919\n",
       "16     x0_Greater Boston Area, East Coast, New England  1.255308\n",
       "40                                      x0_Southern US  1.276127\n",
       "1    x0_Asia-Pacific (APAC), Association of Southea...  1.306695\n",
       "58                               x1_Data and Analytics  1.318850\n",
       "49                                             x1_Apps  1.348741\n",
       "5          x0_East Coast, New England, Northeastern US  1.353684\n",
       "3                    x0_Central America, Latin America  1.386521\n",
       "44                           x0_West Coast, Western US  1.395299\n",
       "26   x0_Greater Philadelphia Area, East Coast, Nort...  1.416959\n",
       "19   x0_Greater Detroit Area, Great Lakes, Midweste...  1.427189\n",
       "17   x0_Greater Chicago Area, Great Lakes, Midweste...  1.451123\n",
       "50                          x1_Artificial Intelligence  1.476063\n",
       "103                                          x3_51-100  1.479495\n",
       "25   x0_Greater New York Area, East Coast, Northeas...  1.549481\n",
       "100                                         x3_251-500  1.599569\n",
       "82                          x1_Science and Engineering  1.618483\n",
       "51                                    x1_Biotechnology  1.628098\n",
       "66                          x1_Government and Military  1.643777\n",
       "43            x0_Washington DC Metro Area, Southern US  1.648373\n",
       "98                                          x3_101-250  1.803869\n",
       "59                                           x1_Design  1.812172\n",
       "38   x0_San Francisco Bay Area, Silicon Valley, Wes...  1.813870\n",
       "39   x0_San Francisco Bay Area, West Coast, Western US  2.068642"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.sort_values(by=\"Coeff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                         Features     Coeff\n",
      "0                              Number of Articles  0.874373\n",
      "1                              Number of Founders  1.064404\n",
      "2                        Number of Funding Rounds  0.865582\n",
      "3                                    Years Active  0.963760\n",
      "4           Last Funding Amount Currency (in USD)  1.121737\n",
      "5    Last Equity Funding Amount Currency (in USD)  1.172154\n",
      "6   Total Equity Funding Amount Currency (in USD)  0.684010\n",
      "7          Total Funding Amount Currency (in USD)  1.002564\n",
      "8                        Number of Lead Investors  1.526942\n",
      "9                             Number of Investors  1.573769\n",
      "10                  BuiltWith - Active Tech Count  1.072530\n",
      "11                       SEMrush - Monthly Visits  1.394395\n",
      "12            SEMrush - Average Visits (6 months)  0.904452\n",
      "13                       SEMrush - Visit Duration  1.071131\n",
      "14                   SEMrush - Page Views / Visit  1.032734\n",
      "0.6472072072072073\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression().fit(x2_train, y2_train)\n",
    "print(pd.DataFrame({\"Features\": num_vars.columns.tolist(), \"Coeff\": np.exp(clf.coef_[0]) }) )\n",
    "prediction = clf.predict(x2_test)\n",
    "print(accuracy_score(y2_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 105)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yC7Rs6au7Pfecv9n33ONvfM+e3vIGuU8hL4KUXBIRERERERGJkxBg6VJf+Cu7bdt8+9KlXmGUH6NGefJo2TKfhZRdw4a+Gn32Lqpdu3w1tdq1vTKpcmUftN2mDdSqdWivSyIWL4Z77/XKpEWLfFu1anDaaZ5cOu88uOCCYl/mpeSSiIiIiIiISB42bvRLfmVmwqxZPldo61b46ScfUA2eNFqyxPenpcHMmYUf78UXe3KoUyc4+mjo3Dn3mUlSiELw/sKUFK9MOu44uOgi7w/88ENISoJrroEBA6BLl+jQqawKpWJOySURERERERERYMsWX/Vs507405/8+//69V4JVFjKlPEKojJl/HFPOgl27PA8RHaVKkGzZn6pVSv/K6ZVrlx4sUo+hAAjRsBXX8GaNb6tYUPo3j16e+3aEr/knZJLIiIiIiIiUiLt3g2//OK3v/sOJk/2CqSvv/Zl7qdN831ZHUm7d+/7GOef76ubNW8OjRvn/7nT0qBbN88pVKvmSSLwuUeqIiqGMjJg6tTozKRy5eC99/wfePduOPFEr0pKToa2bfdOJpXwxBIouSQiIiIiIiLF1KZN3oUUgl9mzYIXX4Sff97/OW3aeDJpxQoYOtSHWnfr5vsyM72LKTnZk0+dOkW7l6SUSU+PZgFvuw2efBK2b/f77dv7EnlZ/v3vwx9fEaPkkoiIiIiIiBR5IXil0ddf+4Dr776Dzz/f+xgzX1yrenVPGPXt6wUnXbr4/n79fIayyD527PAfqtRUz1hOnuyru9Ws6RnJCy/0rGNSkk9Bl70ouSQiIiIiIiJF1pgx3nG0Z8/e2+vXh+uvhwYNfD/4bX3vl3zZvNkrkypXhnfe8blJGRk+DKtbN7j8cu9tBLjkkvjGWgwouSQiIiIiIiJxlZEB774Lb7wBM2Z4y1rO+UdduvhIm759/bpBg1IxykYKy7p1MHZsdDW3H3+EV1+Fc8+Frl3hppu8MqlvX6hRI97RFjtKLomIiIiIiMhhs3QpfPutF40sWQJ/+IO3vGU3eLB3KR17rBeW9O0LAwfGJVwprlau9BlJbdv67axp7BUrQu/ecMcd0WFb7drB/ffHL9YSQMklERERERERKRRZSaKMDJ9/vHVrdN+qVfDMM7mf16gRnH02XHutr8qmiiQ5aL/8El3JLSUF5s+H4cPh7bf9B+yJJ6BHD0hMhAoV4h1tiaPkkoiIiIiIiByUDRt81jHA3Lm+QltCAnzwwYHPHTwYzjkHevb0ldiqV4dmzWIbr5QwIXjyaMEC+NWvfNupp/rE95o1fej2ZZfBccdFzxk5Mj6xlhJKLomIiIiIiJRSmZm5b1+wAH74wb+rlyvno2p++MGHaJcp4wml3LRt6x1GvXtD69bw6197F5LIIVuwAEaPjlYnrVoFVavCxo3+Q/rkk55Y6tTJf0jlsFJySUREREREpIT77jv45JNo29pHH8H06Qf/OLVqQa9ecPTRvjr7Mcd4C1zbttC9u9rZpJBkZnpmMyUFLr3Uk0ivvQb33gtNm/owruRkv5Qt6+ckJ8c35lJOySUREREREZEiLDPTE0Nbt/rtrVthyxZfST3revv2vc+ZPt2TPgkJXvCRJet7eFbF0pAhPiw7pxCiCaM2bXz4tkhMLVsGo0Z5VdK4cf7DDT50e+BAuPxyuOACaNVKWcwiSMklERERERGRIuTTT2H9er+9ezf87W++anpO5cr5iuk1akCVKnt/365Sxb+rn3iiVxeVKeOrsnXpcnheg0iedu6EiRM9kTRggF9WrYJbb4UOHWDECK9ESkrySiWIrvYmRZKSSyIiIiIiIjG0Zo0nembN8vEw69Z50mjMmL2/L+/c6dv259NPvZqoWjUfgl2xogo4pBhJS/O2ttRUmDTJPwRmcN99nlzq1s0/LPXqxTtSKQAll0RERERERArRtGl+CcE7e3IuUmXmlUSZmTBjBnTs6NtD8GHYtWvDI49Aw4a+vUoVX0ldpNjYuNGnwKemejb0zju9R3PUKP/BvvZar0zq188HeYH3bCqxVGwpuSQiIiIiIrIfIXhFUZbt233VtJUr/f60aTB5sremgSeMPvts38cZNgwuvhg6d4bmzaOzj0RKlIcegjfe8KFfIXhC6YwzfJ+ZDwDTAK8SScklEREREREpVXbtig60Bi+yGDMG0tO90ui992DJEk8AbdjgA7Tzo0cPvx4xAs4+2yuSqlf3wgwlk6REWbbMq5JSU30g2Lhx/kOe1dZ2zz3e6tazp/dvZlFiqcRScklEREREREqsTZvggQf8u/DGjTBhgieM8uOCC7yjp2nT6GyjihWhfn1o1iw6L6lpU31nlhIsBL82g3fegT/+ERYt8m3Vq0P//v5Bq1PH+zmlVFJySURERERESqTNm714Yvp0/17cvTu0bu0zjK64Yu+EUPXqcPLJfl21qs9EEimVQoA5c6KVSamp3uqWnOwJpC5d4Pe/9/tduqgsTwAll0REREREpIQJAWbO9NlH06dDnz4werRXIYlIDpmZPlisalWYN8+HbK9b5/saNfIMbeXKfn/wYL+I5KDkkoiIiIiIlCiTJkHv3tH7L7+sxJLI/6Sn+1T6lBSvSho71qfNP/ootGwJQ4d6q9uAAdCmTbQnVCQPSi6JiIiIiEixtXOnD+iePRuef95b4X76yfc99hj07Qvt28c3RpG42rULli6Fdu28rK99e1i82PcdcQT85jdw/PF+PyEBXnopbqFK8aXkkoiIiIiIFGlpaXD//b4AVf360REv27btu5JbQgIMHAjHHQeXXx7t5hEpNbZvh+++i85LmjDBJ9DPn+9VSLfdBjVrQlISNGwY72ilhFBySURERERE4mL3bq84mj0btmyJbg/BF5/6v//z5ND69dF99ev7DKUsW7d6507lyr59wAAN45ZSZvNmTyadeKInj0aOhBdf9A9C9+5w9dX+wQjB9196abwjlhJIySUREREREYmZn36Cr7/2ES8JCb7t22990amvv877XDNo0gROOcWTSrfcArVrxz5mkSJt0yb/8KSm+tykadNgzx7P0h51FFx1lbe69e3ryx+KHAZKLomIiIiISKHasweWLIH334frr997X506Pk945kzvyunUyQsr6tTZ+7jq1dXSJgLAypWeSOrRA9q29YTSGWdAxYpernfHHV6Z1KqVH9+jR3zjlVJJySURERERESkUixfDf/8Ld98dXckc4NZb4dproW7d6LwkEdmPnTvhnXeiM5Pmz/ftDz4IN98Mgwb5Cm+JiVChQnxjFYlQcklERERERA7ZTz95wcSOHX4/IQHefReOOUYzg0X2KwSYN8+TSDVrejsbwO9+B1Wrennf5Zd7ZVLXrr6venXo1y9+MYvkQsklERERERHJ0+7dXpG0YIEXSuzYAcuW+ermWddr1/qxJ54Ir70GtWpBOX3bEMndSy/Bp596Umn1at928smeXKpUyecntW6t6fRSbOjXvYiIiIhIKbZhA0yd6tfvv+8VR2lpnlDKunz++b7n1awJTZv6CueJiX6dlOQFFiISkZHhA7dTUuDnn+Fvf/Pt773n248/HpKT/dK+ffS8tm3jE69IASm5JCIiIiJSSqxe7YO0P/4YvvwSZszY95iKFT1RlJDgVUoJCf691wwefxxatPBtVase/vhFio2PPoJ//APGjYOtW31bu3Y+T6lSJXjrLahSJb4xihQiJZdEREREREqwH3+E5cs9mfTEE76SG0CTJtC9OzRo4BVH/fpB7dq+epuI5NPOnTBhgre3paTA8897O9uqVb5k4nnneXY2Kck/dFmUWJISRsklEREREZESIgTYtMmTSY8/DuvXwwcfRPdfdBGMGAF16vhsYLP4xSpSrM2cCVdcAZMmQXq6f5i6dvVlElu39oHcl14a7yhFDhsll0REREREiqF162DOHNi2De6802cmrVgBu3btfVz37nDttTBkCNSvH59YRYqtDRtg7NhoZdJvfwu//z3UrevzlK6/3iuT+vXzQWRZlLmVUkbJJRERERGRImzxYvjiCx/fUr68b1uyJLrAVJZy5eC666BxY780bQq9e0PZsoc9ZJHiKy3Nh41lZkLPnvDDD14SWKEC9OrlSSWAhg29HU5EACWXRERERESKpKVL4aSTYNasvbcPGeJzkTp1gg4doFYtX628a9do8klE8mnpUq9Kyro0agRffeVZ2b594fTTvTKpZ0+fdi8iuVJySUREREQkDjZsgFGjYMcOH7o9ZQrUqBHtpvn+++ix773nK5ZXqaJuG5ECCwGWLfPlEAHOP98/hOAfvv79/YOW5W9/O/wxihRTSi6JiIiIiBSyELx1bfNmWLjQW9aWLoV//9uLIvanShUYONBvn3wyHHust7opoSRSACH4YLKUlGhl0sqVsHGjJ5NOOw0SE70yqXNn9ZCKHAIll0RERERECig9Hb7+2odoL14Mo0d7RdKBRrH06gUDBvj839/+FmrX9o4bJZFEDkFmJsyYAS1aeL/oM8/AlVf6vsaN/UOXnBz9oJ15ZvxiFSlhlFwSEREREcmnPXvgk0/ggw98BfLp0/c9pk8fuOACn3+U9T32qKN8X/360KSJz0gSkUOUkeH9o1kruY0d6+WCr78OI0b4gLIXX/QPYuvWyt6KxFBMk0tmNgR4AigLPB9CeCDH/hrAKKB5JJZHQggvxTImEREREZGCWL4crroKPvxw7+1JSfDww55MqlcvOs5FRArZrl2e1a1c2dvZli/3JREBjjgCzjrLE0nHHefbWraEiy6KW7gipUnMkktmVhb4O3A8sAyYbGYfhhBmZzvsamB2COEUM6sHzDWz10MIu2MVl4iIiIjIwUhP97lH//iH3z/mGPjnP311No1oEYmxr77y3tPUVJg4EdLSYPhwePttb3/74APvM23QIN6RipRqsaxc6gksCCEsAjCzt4BTgezJpQBUMzMDqgIbgIwYxiQiIiIikm87d3ohxJQpfn/sWOjXL74xiZRYmzbBuHE+dPt3v/NtN9zgc5S6d4drrvG5Sdk/hMOGxSdWEdlLLJNLTYCl2e4vA3rlOOYp4ENgBVANOCuEsCfnA5nZZcBlAM2bN49JsCIiIiIiWb75xld3u+EGWLfOty1e7IUSIlKIvv3Wl1FMTYUff/QV3mrXhosv9uFkb73lg8qqVYt3pCKSh1iOEsxtWlrIcf9E4EegMdAVeMrMqu9zUgjPhhASQwiJ9erVK/xIRURERKTUWrMG3nwTmjaFDh2gbl0YNMhXcVu3zretXq3EksghW7HCk0VXXQXbtvm2L76AZ5/11d3uustb4JYti069P/JIJZZEioFYVi4tA7KPM2yKVyhldxHwQAghAAvM7GfgSGBSDOMSERERkVIuRP6X59KleyeNNm2Ck06C3bv9+2/bttCmTXxiFCkRZs2Cxx7zyqQFC3xb1are9ta9O9x0E/zpT5CQEN84ReSQxDK5NBloZ2atgOXA2cCIHMcsAY4FvjWzBsARwKIYxiQiIiIiJUwIXgSxeLF32KxaFd2ekeELTKWlRS8ff+yrlWfXvz/8/e9w9NFarVykQEKAefMgJcUTSeedB0OG+OCy99/3ZRWvvNKHmHXtCuUiX0Wr79O4IiLFUMySSyGEDDO7BhgNlAVeDCHMMrMrIvufBv4PeNnMZuBtdLeEENbFKiYRERERKf4yMuCnn2DqVPj+e3jhBdi+fe9jshJE5cpBxYpQoUL0Ureuj3Tp3RtatYJmzbwFrnLlw/9aRIq9bdvgoos8obRmjW9r0AAGD/bb3bvD2rXRNjcRKZFiWblECOET4JMc257OdnsFcEIsYxARERGR4iMjw1vTli/3+2vXeiXS/Pnw3XdecTRjhhdDgCeEunXzkSz9+sFRR0HfvtCoUfxeg0iJlJHhA7dTU706qUULePJJqFLF+0tPOMFXcktOhnbtohleJZVESoWYJpdERERERLJLT/fOmfT06LatW+HDD2HUqGhL2/5UqgRXXOHFED16QPv2ULZsbGMWKZX27Ikmhq680j+gWUO427aFTp38thlMmBCfGEWkyFBySUREREQKXWYmfPSRfxedPx+eftqrkELOtYNzSEqC00+H5s2j32srV/b2tcaNfQ6wiFR7TRgAACAASURBVMTAjh2eJEpN9cvcuV6RVKaML6V4/vlemZSU5B9GEZFslFwSERERkQLJyPDL0qWwZQtMmgRffglz5sDs2fsen5QE5ct7G1u/ftGumUqVvJ2tWTMN0xY5bLZs8Q9f+fI+zf76672ksEwZH7g9fLgnnKpWhdtvj3e0IlLEKbkkIiIiIvmSmekJohUrYPduWLky9+O6d4dzz/Wumptv9u+m1atD/fqHN14RyWbDBl9OMasyaepU+Pprn5HUrZsnlwYM8MxvjRrxjlZEihkll0REREQkVzt2eCXShg2eKLr4Yt9u5quM79jhg7Nr1oQjj/T2tWOO8Q4aEYmzVav8g9u4sSeSevTw7RUq+FKJt98OTZr4tr59/SIiUkBKLomIiIjI/4QAr74Kl13mLW979uy9//TT4Y03oGLF+MQnIvuxZEm0KiklxSfn33gjPPKID9++7z6vUurZ0xNMIiKFSMklERERkVLuiivg3/+GXbu8GikrodSnD9x7L7Rp4/fLlfOqJM1FEomzEGDhQq9O6t/f73fvDuvXe0tbUhL87ncwZIgfn5CguUkiElNKLomIiIiUMCHA1q2wfTssXuzJoMWLfeW2rVt9ZlL2y7x5ft7110OVKj4j6eyzoUWLeL4KEdnLvHnep5qS4tVJK1dCu3a+3Qxeftmzv0cfDWXLxjtaESlllFwSERERKcZ274YffvBqoy++8A6YrVvzPqdiRR/D0rixLwp1+ulw4YU+N0lEioDMTJg+HcaPh6uu8uTRAw/ASy/5nKRBg7zFLTk5es7QofGLV0RKPQshxDuGg5KYmBimTJkS7zBERERE4iIEWLYMli6FtWth5EgftZJdq1Y+u7dPH7/foYNf160LrVtDrVpqbRMpchYtgnff9aqksWNh82bfPncutG8PCxZAmTL+AdcHWERiwMy+DyEkFuRcVS6JiIiIFFGZmZCe7rOQxo+Ht9+GDz6IfufM7rPP/LpdO08giUgRtmsXTJzoiaQzzoCOHWHaNLjlFi8hPPtsr0pKSoJmzfyctm3jG7OISB6UXBIRERGJg4wMb2ebPdsXdDLz1rbduz2hlJ6+70ptACNGQL9+XrxQtqzPR+raFSpXPvyvQUQOwsaN8OijnlCaONE/7Gben9qxI5x4IqxeDfXrxztSEZGDpuSSiIiISIxt3w533eWVR8uWQZ06kJbmA7az9OsH3bpB+fJ+SUjY+/bu3XDKKb6iuIgUcZs2eWtbaqovt3j55T7s7LHH/EM8cqRXJvXv732q4BliZYlFpJhScklERESkkGzf7pVIs2b5UO033vDtEyZEj6lWDXr29Na1pCRo0ACOOAIaNYpPzCJSiO64Az7+2FvcQvDM8O9+5/sqVYL16/1aRKSEUXJJRERE5BA8+6x/j/zHP3LfX6+ej1Fp3RrOOceTSyJSzC1f7lVJKSmwahW8/75vnzfPSxPvvtsrk3r12juZpMSSiJRQSi6JiIiIHKSxY/075Z//DDt3+rZateCYY2DYMF+prXVrX52tTJn4xioihyhrdW0zeO45ePBBWLjQt1Wr5iWI6enew/r22/GLU0QkjpRcEhEREcmn9HRflW3YML9fuTIMHAivvQZNm8Y1NBEpLCHA3LnRyqTUVL+0auUf+k6d4OqrvTKpSxcop69UIiL6TSgiIiKSh8mT4fbb4Ysv9t5+003w8MPxiUlEClHWMo0VK8L48XD66bBmje9r2BAGDPAJ/ADnnusXERHZi5JLIiIiUiqF4KNSQoA5c+D1131cSpZx46Bq1eiKbmXKeLvbMcfAJZf4ym4iUgxlZMAPP0Qrk8aOhTvvhOuu837WE0/0hFJyMrRt6+1wIiKSJyWXREREpFTISiZt2QJvvQWPPuorumU3aFB0RtKxx/pq4n37wqmn+n0RKYbS0mDtWu9dTUvzaqRNm3xfu3ZwxhnQtavfb9gQXn01frGKiBRTSi6JiIhIibJkCTzwAEyf7sO2ExL89o4d+x7bty9ccIHf7t4dEhMPb6wiEgPbt8OECdFZSRMmQJ8+8NVXUKGCL9/YqpVXJjVqFO9oRURKBCWXREREpEQIASZO9O+Q4Eml3bth8GDo1w9WrvSWtj59oHZtGDrUv2eKSDG3ZQtMm+artgGceSaMHu1liN26wZVXwnHHRY//4x/jE6eISAmm5JKIiIgUK9u3w3//C59/7gmjNWuil127/Jhhw+A//4m2uIlICbJx494ruf3wg2/fsAFq1ICbb4aRIz2rXKNGfGMVESkllFwSERGRYuHHH+EPf4AxY/x+nTrQsiXUrw8dOvh1vXpw1FFwyilxDVVECtPKlfDttz4UrV49n77/+9/76m69e8Of/uQtbpUq+fGDB8c3XhGRUkjJJRERESlyNmyAzZthxgx46SUvVEhJ8X0VKsCLL8JZZ0HZsvGNU0RiYMsW+OCDaHXS/Pm+/fXXYcQIb3vr2tX7XNXbKiJSJCi5JCIiIkXG7t3w7rtw7rn77rvoIp/De8QRhz8uEYmREGDBAk8ktWzpyzJu3Ai//S3UrOlzlC67DAYM8PlJ4EO4NYhbRKRIUXJJRERE4mLzZhg1Cj76yFvadu6EL7/075UAV18NRx/tK7h17w5m8Y1XRApJCPDMM/DNN55UWrnSt19yiSeXWrTwJR47dFB5oohIMaHkkoiIiBw2W7f698g5c+C00/be16aNz0oaPhyOP95XexORYi4z01dyS031XwB33OGZ4qefhnXrfI7SgAE+Myl7WeLRR8cvZhEROWhKLomIiEihC8FXb3vsMVi2LLr9jTeit+vWhbPP9u+a9esf/hhFJIbeegteew3GjvUZSgBduvjwbTOvWqpRQyWJIiIlhJJLIiIiUiDz58PMmV6FtGcPTJoEVav6vjff3PvYtm39umVL6NEDrrnGF3mqWPGwhiwihW3nTpg40SuTxo6F//wHqlSBWbPg55/hnHO8MikpCZo2jZ5Xs2b8YhYRkUJnIYR4x3BQEhMTw5QpU+IdhoiISKmwaJEnikLwzpb334eMDB+DkpmZ+znt2nmyqV07uPFG6N9fSSSREmfsWLj1Vs8q797tFUhdusC//hX9JVCmTLyjFBGRg2Bm34cQEgtyriqXREREJFebNsGJJ/pCTtk1buwrt9WsCa1b+6yk5s29aqlcOXW5iJQoGzd6Iik11S+33uoD0ypVgvR0uPZan5fUv//e1UhKLImIlCr5Ti6ZWZUQwvZYBiMiIiLxt2ULPPgg/OUvfr9lS2+BA69YUvJIpATLyPAs8YYNMHiwr9oWgk/Y79ULypf343r0gAkT4huriIgUGQdMLplZX+B5oCrQ3My6AJeHEK6KdXAiIiISeyF48mjjRvjgA7j//ui+fv18WznVOouUTMuWRauSUlKgZ0945RWoVQvat4czz/TKpJ49vVpJREQkF/n5U/Fx4ETgQ4AQwjQzS45pVCIiIhJTO3fC8897t8sHH0Ba2t77jz8ePv3UK5VEpIQIAdaujS7POGQIjB7tt6tX99a2pCS/b+bzk0RERPIhX/8fMoSw1Paugd/PCE8REREpirJWdcvMhHHj4KmnfHvduj43ads2eOgh73xJTvbtIlLMhQA//RStSkpNhR07YN06n4l02mmeYEpO9mHcyiaLiEgB5Se5tDTSGhfMLAEYCcyJbVgiIiJSGJYsgcsuixYnZHfXXX7RDCWREmLPHpgxA448EipUgLvvhnvv9X2NGnkSacAAH8RdoQJccUVcwxURkZIjP8mlK4AngCbAMuBzQPOWRERE4iQ9HRYu9Fa2jAz4+WdfCTzLxo3w8cewYoXP4gU45RRPJFWp4vdr1oSGDQ9/7CJSiDIyYOrU6Mykb7/1ZR5TU7297fTTfSnHAQN8WUdlkkVEJEbyk1w6IoRwbvYNZtYPGBebkERERCQ3zz7rhQgrV+bv+Nq1oXt3eOABn6EkIsVcWhpMmuQzk444AiZO9DlJ4MO3f/1rr07q0MG3de3qFxERkRjLT3Lpb0D3fGwTERGRQpCR4ZVJs2b5ZeZMv8ye7fs7d4bf/AY6dvSxKc2bRyuSwMemNGvm85NEpBjLzIRvvonOS5owwRNMN90EDz8MiYnw9tueUFIpooiIxNF+k0tm1gfoC9Qzsxuy7aoOaNqfiIhIIXvkEXjtNZ+/m9XmZgatWnki6dRT4eqroUmT+MYpIjGyebNP3N+5E848038BnHWW97p27+6/AJKToyu6VagAw4fHN2YRERHyrlxKAKpGjqmWbfsW4NexDEpERKSk27MHNmzw74wbN/rtRx7x74rXXuvJpI4d4aij9q5KEpES5ssvfUhaair8+KP/cujQwZNLZcrA559D27ZQvXq8IxUREdmv/SaXQggpQIqZvRxC+OUwxiQiIlJibd8Of/mLX3Jz/fXw0EOHNyYROUxWrvQk0uTJ3tZmBqNGwVtvQe/ecMcdXpnUu3f0nO6aRCEiIkWfhRDyPsCsHnAz0BGomLU9hDA4tqHlLjExMUyZMiUeTy0iIlIg69bBm2/6YO3Vq32MCsCxx8KFF0KtWtFL+/Y+M0lESojJk+GZZzypNH++b6taFebMgaZNYe1ar0qqUCG+cYqISKlnZt+HEBILcm5+Bnq/DrwNDAWuAC4A1hbkyUREREqLHTvghRe81W3Jkuj2I4+Ev/7VixMqVYpffCJSyELw5FFqql9uuMFXalu2DN57z+ckXX45DBjg28tF/gyvVy++cYuIiBSC/CSX6oQQXjCza7O1yqXk58HNbAjwBD4A/PkQwgO5HDMQ+CtQHlgXQhiQ7+hFRESKgN27YepUePZZWLrUK49Gj47u79ABHnwQunTxVdxEpARZscL7WVNTYdUq31a/PpxxhieRTjnFyxfLlIlvnCIiIjGUn+RSeuR6pZmdDKwAmh7oJDMrC/wdOB5YBkw2sw9DCLOzHVMT+AcwJISwxMzqH+wLEBERiYfvv4cbb4SFC2H5ci9ayNKrF/TsCa1bw/PPayC3SImQmekDt7Mqk/r0gZtvhpo1/RfCscd6SeKAAd7faubnlcvPn9siIiLFW37+a3efmdUAbgT+BlQHrsvHeT2BBSGERQBm9hZwKjA72zEjgPdCCEsAQghrDiJ2ERGRwyoz02cnTZniY1TGj4fzzoM2baBlS08mJSZC5crxjlREDlkI0QTROefAJ5/Ali1+v02b6NDtypVhwYL4xCgiIlJEHDC5FEL4OHJzMzAIwMz65eOxmwBLs91fBvTKcUx7oLyZfQNUA54IIbyaj8cWERGJuW3bIC0NvvnG5yd9+ml0X5Uq0KMHvPKKul1ESoSdO2HiREhJ8cqkbdv8PkCdOjBihFcmJSdDkybxjVVERKSI2W9yKdLWNhxPEn0WQphpZkOB24BKQLcDPLblsi3n0nTlgB7AsZHH/M7MJoQQ5uWI5TLgMoDmzZsf4GlFREQKLgT44Qc46SRYk6Oe9tRT/Tvlfff5ym4iUoxt2+ZZYjO45x74858hPd3vd+3q7W179nj2+Kmn4h2tiIhIkZZX5dILQDNgEvCkmf0C9AH+GEJ4Px+PvSxyfpam+LymnMesCyFsB7abWSrQBdgruRRCeBZ4FiAxMTFngkpEROSQ7NgBt93mSaVZs2D9et9evjzcfbffPvlkH8gtIsXUxo0wdmy0MmnqVF/drVUrTyZdf71XJfXr53OUREREJN/ySi4lAp1DCHvMrCKwDmgbQliVz8eeDLQzs1bAcuBsfMZSdh8AT5lZOSABb5t7/GBegIiIyMGaOdMXbxo/3ld1S0317bVrw69/DR07+uykPn2iI1dEpJhZvdozxLVr+7ykoUO9NDEhwafu33qr3wYvSzz11PjGKyIiUozllVzaHULYAxBC2GVm8w4isUQIIcPMrgFGA2WBF0MIs8zsisj+p0MIc8zsM2A6sAd4PoQws8CvRkREJIdHHoGff/bbGRkwaZIv+JTTTTd5V0zWd00RKWaWLo2u5JaSAnPnwuOPw3XX+YC0e+7xVreePaFixXhHKyIiUqJYCLl3mZnZDiBr6QsD2kTuGxBCCJ0PS4Q5JCYmhilTpsTjqUVEpIhatCg6f3fcOKheHUaN8tXd0tL8mLp1fXRK8+Z+feml0Latd8OoA0akmAnBP/hbt/qHeNs2qFHDZyRVrw5JSd7iNmwYHHlkvKMVEREpFszs+xBCYkHOzaty6agCxiMiInJYDR4Mv/yy97aOHT2h1KUL3HADtGgRn9hEpJD89JMv3ZhVnbR8uX/4v/wSqlaFV1+FDh2gc2coWzbe0YqIiJQq+00uhRB+2d8+ERGReNq+HT76CO64A1at8qKFiy6Chx7yqqTateMdoYgcksxMmD7dJ+yfd55v+/3vYcwYaNTI29uSk2HgwOg5554bl1BFREQk78olERGRImfCBB+0DdCgARx1lN+/5BKvVBKRYmruXPjwQ5+XNHYsbN7s2eJhw7zV7eGHvUKpTRtN2hcRESlilFwSEZEi7ccfvRMmy/XX+/UZZ8A77/h3TxEpZnbtgsmTPZF0ySVejfT553DzzXDEETB8uFcmJSd7Ygl8tpKIiIgUSflKLplZJaB5CGFujOMRERH5n5kzvftly5a9t198MTz/vIoXRIqVFSvgn//0eUkTJ0an7XftCkOHevvbb34DDRvGN04RERE5aAdMLpnZKcAjQALQysy6AveGEIbFOjgRESldduyAOXNg7Vqfq3TVVVClio9ZadcuelyNGkosiRRpmzf70o0pKd63etppkJ4Of/kLdO8O11zjVUn9+0eHpNWqFd+YRUREpMDyU7l0N9AT+AYghPCjmbWMWUQiIlJibdkC333nq4UvXQobN/qiTlOnwrx5fh1C9PgGDeDrr71LRkSKuBDgppv8Qzttmn/Qy5eHChU8udSiBWzaBNWqxTtSERERKWT5SS5lhBA2m/4XsYiI5MPKlbB1K4we7YULlSr59tWr4dNP939exYq++lvnztC4sVcmHXGEihlEiqQVK7y9LTXVP6x//7tfjx8PNWvCnXd6ZVKvXlC5cvQ8JZZERERKpPwkl2aa2QigrJm1A0YC42MbloiIFFVr1nib2rvvQkICLF4MixZ5K1tu6tf3xFG1anDFFdClC3Tr5vvq1fP94EmosmUPy0sQkYJ6+GF45hlYuNDvV6sGQ4ZE948fr55VERGRUig/yaXfA7cDacAbwGjgvlgGJSIiRcuaNf6d8vnnvaslS7lyMHAgHHOMVyz16+ezkpKSICMDjj8eWraMV9QiUiAheJ9qVmXSd9/B9OnRCqSOHX0gWnKyD+Mul+3PSSWWRERESiUL2Ydb5HaAWbcQwg+HKZ4DSkxMDFOmTIl3GCIiJdqGDT4PCTxplJQU3deiBVx6KZx+OnToEJ/4RKQQ7dnjl3Ll4KOP/AO+erXva9DAk0iPPw5NmsQ3ThEREYkpM/s+hJBYkHPzU7n0mJk1At4B3gohzCrIE4mISNE1bhy8/jr8619Qty7MnbvvMeedBy+84K1wIlKMZWTAjz96VVJKCnz7LTz3HJx5JjRv7iWHyckwYIAv06hqJBERETmAAyaXQgiDzKwhMBx41syqA2+HENQaJyJSjIQAq1b5sO1Fi2DXLt9++unRY5o08e+dxx3nq4e3a+fbK1WCYcOUWBIplnbvhs2bfcjZypU+KX/rVt/Xtq3/Emja1O936QKvvRa/WEVERKRYyk/lEiGEVcCTZvY1cDNwJ5q7JCJSpO3YAUuX+u2vv4Zrr/XvmLmpX9+rl9q2PXzxiUiM7NgBEyd6VVLWzKThw+GVV6BhQ7jsMkhM9H5XtbqJiIhIIThgcsnMjgLOAn4NrAfeAm6McVwiInKQdu3yldsAJkyAiy7a95hhw+CssyAzExo3hjp1fIW2Dh20UptIsbV1KyxYEF2GsXdvmDHD29m6dvVlGn/1K99nBo88Er9YRUREpETKT+XSS8CbwAkhhBUxjkdERPJp9Gh46SV4++39H3PkkXDnnX570CAvWhCRYm7DBhg7NjozaepUqFkT1q6FMmXg7ruhYkVfvrFGjXhHKyIiIqVAfmYu9T4cgYiISP589RU884wP3wafi5SRAeee6ws+HX20z1fq0wdatoxrqCJSGFat8qHbp5ziSaMHH4SHHoIKFaBXL7jtNh++nbUC8BlnxDdeERERKXX2m1wys3+FEIab2QwgZN8FhBBC55hHJyIie0lPh7/+1VcLP/ZYuO46GDo03lGJSKFavx4+/dQrk1JTo8s3pqb6nKRLLoGTT4aePT3ZJCIiIhJneVUuXRu51tcWEZE42bPHO2BGj/bFnm67za+7dYMxY+IdnYgcshBg4UJPHHXr5pc5c+D8872lrX9/TyYlJ0P37n5O+/Z+ERERESki9ptcCiGsjNy8KoRwS/Z9ZvYgcMu+Z4mIyKH473/h6afhl198pbdNm/Y9pkUL+PLLwx+biBSS3bvhhReilUkrIiMtb7/dk0s9e/ocpc6dNWlfREREigULIeR9gNnUEEL3HNumx6stLjExMUyZMiUeTy0iUugyMuCdd+C++2D27Oj2oUM9iVS3rs/nrVcPzjwTqldXF4xIsZKZCdOn++DtChXgyiu9JLF+fb8/YIBXJQ0Y4BP4zeIdsYiIiJRSZvZ9CCGxIOfmNXPpSuAqoLWZTc+2qxowriBPJiIisHw5XHWVj1FZswY2bvTtCQnw29/Co496EklEirEXX4T33vNV3TZv9m3HHefJpTJlPJtcr56SSSIiIlIi5DVz6Q3gU+B+4I/Ztm8NIWyIaVQiIiXY+PHw4Ydw4ok+m/eEE3zF8MaN4x2ZiBy0Xbtg0iRvb5s2zZdxNPPV3RYuhLPO8sqk5GRo1ix6Xv368YtZREREpJDllVwKIYTFZnZ1zh1mVlsJJhGRgxOCVystWuT3H30UOnaMb0wiUkCffAIPPAATJ/oMJTOfkbRhA9SpA889B+Xy+jNLREREpOQ4UOXSUOB7IADZ67YD0DqGcYmIlChbtsCoUXB1tnR95crxi0dE8mnTJhg3ziuTUlLgqacgMdEHpu3aBSNHelVSv35Qu3b0PCWWREREpBTJa7W4oZHrVocvHBGR4i0tDR5+GNavh8WLYds2mDcPliyJHnP//b66eCv9dhUpevbs8ZlICxbA8OHw449edli+vK/itnOnHzdsmF9EREREJM/KJQDMrB/wYwhhu5mdB3QH/hpCWHKAU0VESqyMDE8k/fwzzJgBY8b498+XXooe07EjVKniiaSOHaF9ex+zkpSkGb4iRcby5V6VlFWZdMYZvnxj48be3nbXXb6SW69eUKlSvKMVERERKZLyU7P9T6CLmXUBbgZeAF4DBsQyMBGRouiFF2DmTPjrX3Pf36sXDBoEf/qTJ5ZEpAgJwVduq1nTbycmwtSpvq9aNc8Ed+jg9ytXhi++iF+sIiIiIsVIfpJLGSGEYGanAk+EEF4wswtiHZiISDxNnuwVSVleecW/a372WXTboEFw0knQuze0aaPV3kSKnBC8LzUlJVqdVLMmTJ/u5YOnnw7nneczk7p00ZwkERERkQLKz19RW83sVuB8IMnMygLlYxuWiMjht24dnHaar+a2cmXux/Tp4wtEJScf3thEJB/27IHZs70P1QyuuAKefdb3NWjg7W0DB3rSycxLDEVERETkkOUnuXQWMAK4OISwysyaAw/HNiwRkdjJzPROmG++gXfegVq1fPvnn0ePufBCGDLEk0ng832bNNGsJJEiJSPDB25nVSZ9+y1s3OgZ4lat4Kyz4JhjPBvcrp0+wCIiIiIxYiGEAx9k1gA4JnJ3UghhTUyjykNiYmKYMmVKvJ5eRIqhPXtg4UJYswbGj4ebb47uq1TJF3/q3duLGTp2hEceiSacRKQISUuDKVM8cdS4Mbz9Npx9tu9r186TSAMG+CpuNWrEN1YRERGRYsbMvg8hJBbk3PysFjccr1T6BjDgb2b2hxDCuwV5QhGRWNmzBzZt8mTRokVepPDBB54symnAAHjrLWjY8PDHKSL5lJ7u1UhZK7lNmAC7dsETT8DIkXDssf5BTkrS0DMRERGRODpg5ZKZTQOOz6pWMrN6wJgQQpfDEN8+VLkkIlnS0+Gf//TWtp07Yc4c2LEj92OHDYOrr/aEU8eO+h4qUiRt2eLlhQkJMHiw388qI+za1bPCycl+qV07vrGKiIiIlDAxrVwCyuRog1sPlCnIk4mIHKoNG2DcOFiwAF5+2Rd9Ai9guPRSaNnS5yNlZsLRR0P16n5dqVI8oxaR/fr0UxgzxiuTfvjBSxCPO86TS9Wr+3C0zp3V5iYiIiJShOUnufSZmY0G3ozcPwv4JHYhiYjsa/Jk6Nkz930zZ3o1kogUcatWeYvbwoVw662+7dFHYexYH3x2++1endS7d/ScpKT4xCoiIiIi+XbA5FII4Q9mdgbQH5+59GwI4T8xj0xEBG93e/NN+Pprv9+tG5xzjn/fbNUKqlWDypXjG6OI5CElBUaN8qTSvHm+rXp1uO46Lyl8+WWoVw8qVIhrmCIiIiJScPtNLplZO+ARoA0wA7gphLD8cAUmIgLwr3/BJZf47cqV4csvtZKbSJEUglckpaR4Iun++3242bRp8O67nhG+9FKvTOrWDcpF/gRp2jS+cYuIiIjIIdvvQG8z+xZ4FUgFTgH6hhDOOIyx5UoDvUVKrowMeOYZH9C9bh2sXOkzlsAXierUCapUiW+MIpLDvHlw552eUFq50rfVqwfvvQf9+/vqbgkJPgxNRERERIqsWA30rhZCeC5ye66ZTS3IE4iIHMiePfDaa/DZZ76qeJbOnb3w4YYbfN6SWfxiFCn1MjO9Cik11auTzjwTzjvPE0djx8KgQdGV3I48MvqBrVgxvnGLiIiISMzllVyqaGbd8DlLAJWy3w8hKNkkIods3To49VRffTzLpElwzDHxi0lEssnIgNNPQuBuQwAAIABJREFU96TSli2+rXVrGDLEb7dsCUuXKvsrIiIiUorllVxaCTyW7f6qbPcDMDhWQYlIyRMCrF4Nc+dCWpoXQTz4oBdAZFHrm0gc7dzpmd2syqSaNX1WUrlyUL68T9LPqkzKOSdJiSURERGRUm2/yaX/b+++w6suzz+Ovx+2EUSGooIoIiKogBgZynBWxQEWq7hat9ZR9/pVq1VbR622ap3UVSto1VpnsS4CKgIiIAKi4mIoiIAoBgh5fn88JyYgI4QkhyTv13Xlyvmuk5vxvfB8vJ/7G2PcuzILkVR9jR+f5veuzuWXw1VX+bAoqVLl5xcvWTvnHLj3Xli6NAVFnTpB9+7F5z71VHZqlCRJUpWwps4lSSqzf/87Pdlt4cL0FHKAnj3h6quhYcO03aABdOninF+pUixYkGYjFXUmvfdeWpeak5PCpN/8Jj3Jbc89fSSjJEmS1onhkqRyN2UK/PznaXlb48bpwVEXXggXXQS1a2e7OqmGmDMnJbk5OfDgg3DSSWl9ar16aUL++een7qWcHDj11GxXK0mSpCqsQsOlEMKBwF+B2sDgGOMNqzlvd2AUcFSM8YmKrElSxZo7F7p2Ta+vugouvji79Ug1xsyZqSMpLy99TZmSZiYNHJiWuF19dZqX1L07bLRRtquVJElSNbLWcCmEEIBjge1ijNeEEFoDW8QYR6/lutrA34D9gRnAmBDCMzHGyas470ZgWBl/DZKyaPHi9Pn1iSfgnXfSuJalS+G+++BXv8p2dVI1FSN8+mmajL/99vDxx+k7wCabQK9e6Qbs0iXt69ABfve7rJUrSZKk6q00nUt3AoWkp8NdAywCngTW9qDwbsBHMcbpACGEoUB/YPJK551TyveTtAGZPh2uvx4GD15x/5ZbwnPPwUEHZacuqdqaOrV4XlJeHsyYkQKkBx+E7baDO+5Ig806d3b9qSRJkipVacKl7jHGriGEdwFijPNDCPVKcV1L4IsS2zOA7iVPCCG0BA4nBVeGS9IGLj8f3n4b3ngDfvvb4v377w/33ANt2mSvNqlaKSxMA7c//xwOPTTtGzAAPvgAttgiDd7u0wf22ScdCwHOOit79UqSJKlGK024tCyzdC0ChBA2I3UyrU1Yxb640vZfgEtjjMvT6rvVvFEIpwGnAbRu3boUP1pSeVi2LK28ue221BSxsoMOSk8oL3qauaT1MGkS/Pe/qTNp5Mj0dLemTdMgs1q1UptgixZp+dsa/s2UJEmSKltpwqXbgH8Dm4cQ/gAcAVxRiutmAFuX2G4FzFrpnFxgaCZYag70CyEUxBifLnlSjPFe4F6A3NzclQMqSeWkoCAtafvHP6B+fRgyZMXje++dHjK1xx6paWLTTbNTp1TlLVkCY8ak5W0XXphuuIcfhj/9CXbYAY44InUn9e6dgiVIc5QkSZKkDVCIce1ZTQhhR2BfUjfSKzHGKaW4pg4wLXPdTGAMcEyM8f3VnP8g8NzanhaXm5sbx44du9aaJZXO8uVpFnCMcMEF8MILxce22y41Tpx3XnrAVNG8YEllMH06PPRQCpRGjUrrTCGFTLm5MGtW6kjacsvs1ilJkqQaKYTwTowxtyzXluZpca2BxcCzJffFGD9f03UxxoIQwtmkp8DVBu6PMb4fQjgjc/zushQsqXx8+in83//9tDsJ4N1300xgV95IZbRwYRpOlpeXZibtuWcKj667DnbdFX7969SZ1KsXNGuWrtlqq+zWLEmSJJVRaZbFPU+alRSABkAb4ANgp7VdGGN8AXhhpX2rDJVijCeUohZJ5eChh+CEE4q327eHq65Kr3fZBXbeOStlSVXb99/DlVemmUnjx6eh3HXqpNBozz2hRw+YPx822STblUqSJEnlaq3hUoxxl5LbIYSuwOkVVpGkchdj+qw7ZQpMmwa//33a/+ijMGiQHUrSOps9O3Ul5eWlZWxXXAEbbQT/+ldaP3rFFWkwWY8esPHG6Zo6dQyWJEmSVC2VpnNpBTHGcSGE3SuiGEnlY/78tOTtgw/SA6fefXfF4wcdBBdfnAZ0S1oHV14Jjz0GH36Yths2hGOOSa9r1UrrTWvXzlp5kiRJUjaUZubSBSU2awFdgbkVVpGk9faXv8DdmQWo++4Lp52WHkbVty/07OloF2mNYoSPPkrL2/LyUko7alRq8Vu0CDp0gNNPT51Ju+6aOpKKGCxJkiSpBipN51KjEq8LSDOYnqyYciSVVWEh/Pe/cO656XNxu3bw3nspVJK0BoWF6XutWvDgg3D55fDll2nf5punEOm776BRo5TcSpIkSVrBGsOlEEJtoGGM8eJKqkdSKRUWwn33we9+l5opfvih+Fj79jBypMGStEoFBTBhQupKGj4cRoyAF1+Ebt1SW98++6Q2vz590s3kUDJJkiRpjVYbLoUQ6sQYCzIDvCVtID7/HJ59Fs4+u3hfly7QpEn6bHz66dCmTfbqkzY4S5dCfn4apj1hAvTunRJZgLZtoX9/yMlJ2z/7WfqSJEmSVGpr6lwaTZqvND6E8AzwL+D7ooMxxqcquDZJGS+9lJ7yduGF6XNykX794PbbYbvtslebtMH54Qd4++3izqS33oILLoDrrkvrRY89NnUl9ekDLVtmu1pJkiSpyivNzKWmwDxgHyACIfPdcEmqBDNnwgEHFG8fcwycf356+rmfiyVSF9LMmbDjjmm96DbbwNy5aTlb585w6qmw//7p3JwcuOuu7NYrSZIkVTNrCpc2zzwpbhLFoVKRWKFVSQLSSp499kivb7opBUsGSqrx5s9PQ8WKnuY2bhx07AgTJ6ah3H/8I2yxBfTqBZtumu1qJUmSpGpvTeFSbaAhK4ZKRQyXpEowe3aasZSTY7CkGmzOHBg1Cg49NHUjnXUWDBkC9epB9+5w2WVpAHeRU07JXq2SJElSDbSmcGl2jPGaSqtE0o++/RaefBJOOilt33mnwZJqkDlz4OWXizuTpk5N+z/5BLbdNg0fO/30FCw1aJDVUiVJkiStOVzy2ctSBfrnP+GWW6BFixX35+fDm2/CkiVp+/DDYcCAyq9PqhQxptBo+HDYa6/0qMNXXklDtzfZJD3Z7cQTVxy+vdtuWS1ZkiRJ0orWFC7tW2lVSDXIV1/Biy+mz8uQRsSU/KwcApxxBvziF9CtG9Stm506pQqzaBE8+mhxZ9LMmWn/3/4GZ54JBx2U5ih16gS1a2e3VkmSJElrtdpwKcb4TWUWIlV3Tz0FJ5yQPlcX+cMf4P/+L2slSRVv+XJ4770UIm21FRxxROpWOvPM1LbXp0+al9SnD3TokK7ZdFPYddfs1i1JkiSp1NbUuSSpjAoK4Isv4Lrr4MMPYeHC9CArgCZN4NxzU2dS0Wdpqdq54w4YNgxGjEg3AMDRR6dwaZNN0lK4rbdOrXqSJEmSqjTDJakcvfxymjP82WepYaNI//7QuTOcemoaISNVG0uWwOjRqTNp9uwUKgE8/XRKWI88MnUl9ekDrVsXX1fytSRJkqQqzXBJWg/ffQf/+U9a6hYC3HYbfPllmkt88MFQvz4cdRQ0a5btSqVy9thjcNddMGpU8fT5Ll1S216dOvDccz7JTZIkSaohDJekMnjuObj1Vnj11Z8eGzgQnnii8muSKsTChfDGG8XDt594Ij217euv4fvv4ayzUldS797QtGnxdQZLkiRJUo1huCSVUozwzTepW+ncc9PStz33hObN4aqrYIst0nmbbZbdOqX1EmNqwxs7Nq3xHD8eCgvTYwu7dYN581K4dNZZ6UuSJElSjWe4JK3GCy+kJ7nNmpW2585d8XjHjjByZOXXJZWrWbNSR1LR1znnpFCpefM0ePvKK9PT3Lp3h5ycbFcrSZIkaQNkuCSt5JtvYMCA9JArgFNOSU0bkFYI9eyZXvfvn536pPWyeHEKifLzoVOn9DhDgIYNoVev4ha8bbeF117LWpmSJEmSqg7DJWklf/1rcbD0/PPQr19265HKLMYUHuXlFc9M2nnn9Be7QQM44AA444w0M6lLlzSIW5IkSZLWkZ8kJGDy5LQaaPToNFMJ0qxiVwGpSikshE8+gbZt0/aAAfDMM+n15pun5W0HHlh8/u23V36NkiRJkqodwyWJ9DCsV1+Fzp3T5/KTTjJYUhVQUJAGbhfNSxoxAhYtggUL0l/g44+Hgw9OodIOO6RB3ZIkSZJUzgyXVKMtWwZDh8KNN6btYcOgRYvs1iSt1tKl6SluO+0EjRvDbbfBhRemY9tvnzqV+vYtPv+II7JTpyRJkqQaxXBJNc7HH8Mjj8DTT6emjyJ//nNaOSRtMJYsgTffLO5Meust+OEHeOopOPzwNFV+q63SzKSttsp2tZIkSZJqKMMlVXsxps/k334LL7+cQiSA+vXT8rcOHeDQQ6F9++zWKbFoUVqj2bw55ObCp5/CPvuk5WxdusBpp6XOpL32Sue3bVs8X0mSJEmSssRwSdVGYSFcdFEaN7NsWfpcnp8PEyak7SKnnJLOcwSNsi5GeO659CS34cPh3Xdh+XI44QR44IH0l/TFF6FHD9h002xXK0mSJEmrFGKM2a5hneTm5saxY8dmuwxtYB54IHUhFdlmm/SVkwPNmsFGG8GgQWme0k47GSopS776Kg3d/uab1IUEqWXus8+ge/e0vK1vX+jZEzbeOLu1SpIkSapRQgjvxBhzy3KtnUuq0saPh2nTioOlQYPg73/3SW/agAwbBk8+mWYmffBB2te6NZx6ako5n3sOtt4aGjTIbp2SJEmSVEaGS6pynn46fUZfsgSuuqp4/333pSVvUlbECNOnpxBp5Ei488402Ovll+Hxx6F3bzj55NSd1LVrcftcu3bZrVuSJEmS1pPL4lSl/POfcNxxK+675BI4+mjo1Alq1cpOXarBxo2Dm29OodLMmWlf8+Zp+duOO8J336V1mbVrZ7dOSZIkSVoDl8WpWjvnnPQArcWLi1cVPf44HHJICpPq189ufaohli+HiRNTiJSXB2eeCfvuC99/D6+/nmYl9e2bOpM6dCjuTGrYMKtlS5IkSVJFM1zSBu+xx9IMpc6d0+f50083UFIlmj8fjj8+LXVbuDDta9MGvv46ve7VK3UsOSVekiRJUg1luKQN0jffpKeyP/AAzJ0Lv/51GmEjVZj8fBg9urgzaeed4ZZboHFjmDcPjjoqdSX16ZMGcBcxVJIkSZJUwxkuaYMxfTqceGL6HD91alqFVKRPn+zVpWpq2TKoWze9PvbY9ES3JUvSdqdOsPfe6XWtWvDWW9mpUZIkSZKqAMMlZc3kyenz/Ntvp7E0jz1WfOzyy9M4m802S+NrijIAqcwWLkxL24YPT51Js2bBZ5+lzqMdd4Szz04pZq9e0LRptquVJEmSpCrDcEmVbtSoFB69/vqK+9u1S80it93mTCWVg6+/hk03hTp14MYb01+6GFNS2a1bmqO0ZAk0aABXXpntaiVJkiSpyjJcUqW78UZ45530/YADYLvtUueSo2u0XmbNSh1JRZ1Jkyentrhu3WCPPeCqq1JnUo8esNFG2a5WkiRJkqoNwyVVusJCaNsWLrkk25WoyooRPv0U6tWDli1hxIjiwVyNGqWlbccfD1tumfb17p2+JEmSJEnlznBJlea77+COO+CNN2CbbbJdjaqUGGHatBU7k774Aq64Aq69Frp2hT//Gfr2hc6d01I4SZIkSVKl8BOYKtRrr6WnuS9cmJpLAPbZx64lrUVhIUyaBAsWpI6k5cth991h0SJo0SLtu/RS+NnP0vkbbwwXXJDdmiVJkiSphjJcUrlatgwKCuCZZ2DoUHj66bR/r73gsMPSwO7zzstqidpQTZwI//tf6koaMQLmz4dddkn769RJjxNs2zZNfndAlyRJkiRtMAyXtF4KC+Gtt+CJJ1KY9OWXKx4/4ojUZHLOOdmpTxuopUthzBgYOxbOPTftu/769JeoXTv4+c/TX5yiOUoABx2UnVolSZIkSWsUYozZrmGd5ObmxrFjx2a7DGUMHgynngq1a8PAgbDzzlCrFuTnw1FHpW0JgClTUvdRXl5KJPPz0/4ZM9JQ7o8/Tk9x22qr7NYpSZIkSTVQCOGdGGNuWa61c0llMnVqWt42bFjanjYNttsuuzVpA/Ltt/Dmm2n49gknQPv2MH58Gr7dpQuccUbqSurdG5o3T9e0bZvVkiVJkiRJZVOh4VII4UDgr0BtYHCM8YaVjh8LXJrZ/A74dYxxQkXWpPUTIzz8cMoLilx2mcGSgDlz4MYbU6D07rtpzWSdOunpbe3bQ//+8M030LhxtiuVJEmSJJWjCguXQgi1gb8B+wMzgDEhhGdijJNLnPYJ0DfGOD+EcBBwL9C9omrSulu6FO66C558EjbZBF55pXg10+TJ0KFDdutTlnz5ZRq6nZeXwqNTToEGDeDuuyE3F377W+jbF3r0SE9yA8jJyW7NkiRJkqQKUZGdS92Aj2KM0wFCCEOB/sCP4VKM8c0S548CWlVgPSqlpUvTU9/mzEk5wTffpP2tW0PHjulp8A88YLBUI513Hrz4YloHCSk4Ovvs9HqTTWDBAqhbN3v1SZIkSZIqXUWGSy2BL0psz2DNXUknAy+u6kAI4TTgNIDWrVuXV31ahRdegIMPXnFfTg68955L32qMGNNw7eHDU2fSokXw1FPp2BdfwA47pCnuffrArruuGCYZLEmSJElSjVOR4VJYxb5VPpouhLA3KVzqtarjMcZ7SUvmyM3NrVqPt9tAff89fPBB6lICmDcPDjmk+PiAAbDHHmk8zsknp6fBqZqKEULmdv3LX+Cmm2D27LTdvDnss0+an1SrVlofKUmSJElSCRUZLs0Ati6x3QqYtfJJIYROwGDgoBjjvAqsRxkPPAAnnbTqY5tvDoMHp+6lWrUqty5VkuXLYcKE1JWUl5dmJ02YAFttBU2awF57pa6kvn1hxx2LgydJkiRJklahIsOlMUC7EEIbYCYwCDim5AkhhNbAU8DxMcZpFViLSJnCo48WB0u//z1061Z8PCcHevUyVKp2li2DggLYaCN4+WUYOBC+/TYd22671LK2ZEna/tWv0pckSZIkSaVUYeFSjLEghHA2MAyoDdwfY3w/hHBG5vjdwO+AZsCdIXVHFMQYcyuqppps+XLo0gUmTYItt4TnnoOuXbNdlSpEfj6MHl08M+nNN+GWW+D009O8pKOPTp1JffpAK2foS5IkSZLWT4ixao0wys3NjWPHjs12GVVKjHD88fDPf8KZZ6axOs5drka++y4NzdpmmzR8e/PNU8AUAnTqlEKkY4+F7muapy9JkiRJqslCCO+UteGnIpfFKYuWLoWpU2HOHLjuutTEAnDiiQZLVd6CBTByZHFn0jvvwAEHwPPPQ6NGab1jhw5pjWOTJtmuVpIkSZJUzRkuVRPLlsH8+fDJJ/Dss/CHP6x4vEsXePxxaNcuO/VpPcydm9Yz7r132j7ssDSEu169NDTr0kth//2Lz7/kkuzUKUmSJEmqkQyXqoH586FnT/jgg7Rdq1ZaCdW+fXqK/P77Q7Nm2a1R6+DLL+G111JX0vDhMGVKajdbsCBNXb/66vSH3L17GtItSZIkSVIWGS5VcddfD/ffD59+CjfckDqTevVKY3dUBcSY2s3y8lJHUtOm8PDDqRupUaP0h/mrX6W0sF69dM0++2S3ZkmSJEmSSjBcqkKWLUuro95/P3UrQXoI2NdfwyOPpJnNqgLmzYMnnyyemTRjRtr/73/DgAHpD3K//aBzZ6hdO7u1SpIkSZK0FoZLVcD770P//vDxx6s+ftFFBksbrMJCeO+9FCLtvHOam/T113D66dCiBfTtm7qS+vaFjh3TNS1bpi9JkiRJkqoAw6Uq4PLLi4Ola66BgQPTsrcQ0r6mTbNXm1ahsBBuvTV1Jo0YkWYlAZx3XgqXdtgBpk2D7bcv/kOUJEmSJKmKMlzaQI0eDW++Ce++m57+1q4dTJ2a5jhrA7JkCYwdm4Kk5cvhyivTH9K996bjRxyROpP69IFttkn7QvCxfZIkSZKkasNwaQMxfTrceGNaQfXWWz89PmCAwdIG5e9/T4OuRo2C/Py0r2/fFC4BjBsHG2+cvfokSZIkSaokhksbgOefh0MOSa/33hv23x+23BIuuyx9b9TIuc5Z8+238MYbqTNp1CgYNgzq14cPP0zHfv3r1JXUqxc0b158ncGSJEmSJKmGMFzKsocfTk+aB3jwweLXyrL//S+le+PHpxlKderA7rvDV19B69Zw/fVwww3ZrlKSJEmSpKwzXMqSWbPg6qvh1VfT9j/+Accdl9WSaqYvv0xPcsvLS91JN94I/fpBw4awySZwxRVpuVuPHpCTU3ydg7glSZIkSQIMlyrdDz+kHOPqq9MqK4BLLjFYqjRLlqRlbbNmpTWI06al/RtvDHvuCQ0apO2ePeG117JXpyRJkiRJVYThUiW79970RHqAq65KjTF1/FOoGDHCRx8VdyUNHw4HHgj33ANbbAFdusCpp6bOpF139Q9CkiRJkqQy8NN0BVi0KD1MLD8fvvgijekpLITPP08NMwAffAA77JDdOqudwkKYPRtatkzbe+xR3B622WYpRNprr7RdqxY89lhWypQkSZIkqToxXKoAw4bB+eevuK9t2xQmbbkltGuXvrSeli+HCRNSR1JeHowYAXXrpgQvhDQd/YQTUqjUvr1zkiRJkiRJqgCGS+Xsgw/gkUfS6/HjU6ZRp44rrsrFsmXwzjuQm5t+Qy+5BG65JR1r2xYOOwz69EmhU506cMYZ2a1XkiRJkqQawMijnHz+OXTuDAsWpO1WrWC77YrnQ6sM8vPh7beLZya99RYsXgxjxqSA6dhj0/fevdNvuCRJkiRJqnSGS+Xggw9gxx3T60aN4MEH4fDDXYW1zhYtSgFSmzZp3eDIkbD//uk3snNnOOWU1JlUtKawa9f0JUmSJEmSssZwqYxihFdeScO6hwxJ+/r3h3/9K439USksWwb//W9xZ9K4cWlJ2xVXwLXXpoHczz4Le+4JTZpku1pJkiRJkrQKIcaY7RrWSW5ubhw7dmxWa1i+HE4/PT0RrsiWW6ZsZIstslfXBm/OnDR0O0Y44ggoKICmTWHJEujePQ3e7tMHevaEhg2zXa0kSZIkSTVGCOGdGGNuWa61c6kMJk0qDpaGDIEePWDbbbNa0obrmWfghRdSZ9LUqWnf7runcKlOHXjjjbTMzeFUkiRJkiRVSYZL66igoDhYevrptBROpG6kTz5JIdLEiXDrrWn/o4/Ciy9Cr15wwgmpO6nknKRddslKuZIkSZIkqXwYLq2jqVPh9tvTOKAePbJdzQZgxAi4++4UKs2cmfY1a5bmJjVrBnfeCY0bQ+3a2a1TkiRJkiRViFrZLqCqKSxM3y+8EFq0yG4tlaqwECZMgNtuS0va3n8/7f/iC3j11dSZdOedac3gnDkpWII0U8lgSZIkSZKkasvOpXWwfHla+VWjfPopnHMOjBwJCxakfdtuC7NmwU47wVFHwdFHQwjZrFKSJEmSJGWJ4VIpLVgAm28Oy5al7Zyc7NZT7pYsgTFjIC8vLXE74AC44AJo0gQ++gh+8Yv0JLc+faB16+Lr7EqSJEmSJJXRsmXLmDFjBvn5+dkupcZo0KABrVq1om7duuX2noZLpfTFFylY2npruOUW2G+/bFe0ngoLoVatNIj74IPT0rYlS9KxXXaB+vXT68aNYcqU7NUpSZIkSaq2ZsyYQaNGjdh2220JroipcDFG5s2bx4wZM2jTpk25va/hUim89x506pRe33MPHHRQduspk4UL4Y03ijuTatVK2yGkZW5nnpme5NarV/G8JEmSJEmSKlB+fr7BUiUKIdCsWTPmzp1bru9ruLQGy5ZB9+7w7rtp+9JLq1CwNH9+WtIGcNFFcOutqVupbl3YfXfYZ5/UtRRCGsQtSZIkSVIWGCxVror4/fZpcasRY2rsKQqWBg+Ga6/Nbk1rNHs2PPYYnHVWWtbWtGnaB7DnnnDllWnp24IF6Rd27bUO4ZYkSZIkCfj3v/9NCIGpU6f+uO/111/nkEMOWeG8E044gSeeeAJI86Iuu+wy2rVrx84770y3bt148cUX17uW66+/nu2335727dszbNiw1Z53++230759e3baaScuueSSH/dPnDiRnj17stNOO7HLLrtUyjwrO5dKKCiAl19OOczcufDZZ1CnDrz44gY4Y+nTT9M8pCZN4PHH01PbABo2TGHS0Uen4gEOPzx9SZIkSZKknxgyZAi9evVi6NChXH311aW65sorr2T27NlMmjSJ+vXr89VXXzF8+PD1qmPy5MkMHTqU999/n1mzZrHffvsxbdo0aq/0MK3XXnuN//znP0ycOJH69eszZ84cAAoKCjjuuOP4xz/+QefOnZk3b165Du5eHcOljJkzoVWr4u0tt4R7702ZTPPm2asLSG1UH35YPC8pLw8+/zwNgDrtNNhjD7j55vQkt113LQ6VJEmSJEnSGn333Xe88cYbvPbaaxx22GGlCpcWL17MfffdxyeffEL9zAOxWrRowZFHHrletfznP/9h0KBB1K9fnzZt2rD99tszevRoevbsucJ5d911F5dddtmPP3vzzTcH4KWXXqJTp0507twZgGaVNFPZFAJ48EE48cT0ukULeOGFlNFkbdVYYSFMnpyGPu26K3zzDbRvn45tvnkKkS6+GPbfP+1r1QouvDBLxUqSJEmStP7OOw/Gjy/f9+zSBf7ylzWf8/TTT3PggQeyww470LRpU8aNG0fXrl3XeM1HH31E69at2WSTTdZaw/nnn89rr732k/2DBg3isssuW2HfzJkz6dGjx4/brVq1YubMmT+5dtq0aYwYMYLf/va3NGjQgJtvvpndd9+dadOmEULggAMOYO7cuQwaNGiFJXMVpUaHS/ffDw89lBqBAE46CW6/HXJyslDMu+/C66+nzqQhggFLAAAPcklEQVQRI1Kg1K8fPP98enrbkCHprmjf3llJkiRJkiSVkyFDhnDeeecBKfAZMmQIXbt2Xe3g63UdiH3rrbeW+twYY6l+XkFBAfPnz2fUqFGMGTOGI488kunTp1NQUMDIkSMZM2YMOTk57Lvvvuy2227su+++61TzuqqR4dLy5Wm29bnnwqabpoep/eY3sPXWlVTA0qUwdmxa6varX6V955yTBm23bQsDBqTupL59i68ZNKiSipMkSZIkqfKtrcOoIsybN49XX32VSZMmEUJg+fLlhBC46aabaNasGfPnz1/h/G+++YbmzZuz/fbb8/nnn7No0SIaNWq0xp+xLp1LrVq14osvvvhxe8aMGWy11VY/ubZVq1b8/Oc/J4RAt27dqFWrFl9//TWtWrWib9++NM/M9+nXrx/jxo0zXFpfc+akhqBLL4X58yE/H5YsKT5+yy1w6qmVUMiECfD006lN6q234IcfoF69NIi7QQO4887UodSyZSUUI0mSJEmSnnjiCX75y19yzz33/Livb9++jBw5km7dujFr1iymTJlChw4d+Oyzz5gwYQJdunQhJyeHk08+md/85jfcc8891KtXj9mzZ/PKK69w3HHHrfAz1qVz6bDDDuOYY47hggsuYNasWXz44Yd069btJ+cNGDCAV199lb322otp06axdOlSmjdvzgEHHMBNN93E4sWLqVevHsOHD+f8888v+29QKVXbcOmll+DZZ+GOO1bcf8kladnbxhvDYYfBDjtUwA9ftAjefDMFSRdckEKjYcPg97+Hzp3TEO4+faBXrxQsAXTqVAGFSJIkSZKk1RkyZMhPuocGDhzIo48+Su/evXnkkUc48cQTyc/Pp27dugwePJjGjRsDcN1113HFFVfQsWNHGjRowMYbb8w111yzXvXstNNOHHnkkXTs2JE6derwt7/97ccnxZ1yyimcccYZ5ObmctJJJ3HSSSex8847U69ePR566CFCCDRp0oQLLriA3XffnRAC/fr14+CDD16vmkojrGo934YsNzc3jh07dq3n7bVXGl+Umwu//CUMHAhbbAG1alVQYZ9+mpKsvDwYNy6tvatdG/73P9h779Q2FUJahydJkiRJkn7sClLlWtXvewjhnRhjblner9p0Lr33XpqDDfCvf6WM5/jj4eGHK+CHffVV+mF5eemJbYcemtbb3XEHdO8Ol1+e5iX16AENG6ZrmjSpgEIkSZIkSZKyq0qHS6+/DsccA/PmpRnZJXXtmsYYlZuCAjjrrBQoTZ2a9uXkpCnghx6anuK2YEHxMjdJkiRJkqQaoEqGS++8AzfdBI8/nrabNUtPfOvcGXbbrXhfZlniuokRpk9Pa+ry8qBRI7j9dqhTJy13a9sWTjwxzUzabTeoWzddF4LBkiRJkiRJqnGqZLh0//3w5JNw8slwww2QecJe2cSYgiGAq66CwYNh1qy03bw5DBhQfO7o0cXnSpIkSZKk9RZjJPhZu9JUxOztKhkuxQhNm6YcaJ0tX54GNBV1Jo0dCx9+CPXqpc6jPn3SV9++0KHDimGSf9klSZIkSSo3DRo0YN68eTRr1syAqRLEGJk3bx4NynnlVZULlxYvhldfXYcLli1L3+vWhaFD4YwzYOHCtG/bbdOT3BYuhM02S4O4JUmSJElSpWjVqhUzZsxg7ty52S6lxmjQoAGtWrUq1/es0HAphHAg8FegNjA4xnjDSsdD5ng/YDFwQoxx3Jrec8qU9H21OVB+flq+lpeXvt58Mw1n6tcPtt8ejjwydSX17g2tW6/fL1CSJEmSJJVZ3bp1adOmTbbL0HqqsHAphFAb+BuwPzADGBNCeCbGOLnEaQcB7TJf3YG7Mt/X8L7w0kuw336ZHd9/D999By1awMcfw047wZIl6dguu6Th2y1bpu3c3PQlSZIkSZKkclGRnUvdgI9ijNMBQghDgf5AyXCpP/BwTNOkRoUQNg0hbBljnL3agsNy9lvyPFyaVzwz6eST4e67oU0bOO882GMP6NUrDWaSJEmSJElShanIcKkl8EWJ7Rn8tCtpVee0BFYbLrUvnAyHHJJmKHXrBhdfDAcfnA7WqpUeHydJkiRJkqRKUZHh0qrGvK/8vLvSnEMI4TTgtMzmkgCTWLYM3ngjfV1//XqWKqkUmgNfZ7sIqYby/pOyw3tPyg7vPSk72pf1wooMl2YAW5fYbgXMKsM5xBjvBe4FCCGMjTE6OEmqZN57UvZ4/0nZ4b0nZYf3npQdIYSxZb22VnkWspIxQLsQQpsQQj1gEPDMSuc8A/wyJD2AhWuatyRJkiRJkqQNS4V1LsUYC0IIZwPDgNrA/THG90MIZ2SO3w28APQDPgIWAydWVD2SJEmSJEkqfxW5LI4Y4wukAKnkvrtLvI7AWev4tveWQ2mS1p33npQ93n9SdnjvSdnhvSdlR5nvvZDyHUmSJEmSJGndVeTMJUmSJEmSJFVzG2y4FEI4MITwQQjhoxDCZas4HkIIt2WOTwwhdM1GnVJ1U4p779jMPTcxhPBmCKFzNuqUqpu13Xslzts9hLA8hHBEZdYnVVelufdCCHuFEMaHEN4PIQyv7Bql6qoU/93ZOITwbAhhQub+c0avtJ5CCPeHEOaEECat5niZspYNMlwKIdQG/gYcBHQEjg4hdFzptIOAdpmv04C7KrVIqRoq5b33CdA3xtgJuBbXxEvrrZT3XtF5N5IeliFpPZXm3gshbArcCRwWY9wJ+EWlFypVQ6X8t+8sYHKMsTOwF/DnzJPIJZXdg8CBazhepqxlgwyXgG7ARzHG6THGpcBQoP9K5/QHHo7JKGDTEMKWlV2oVM2s9d6LMb4ZY5yf2RwFtKrkGqXqqDT/7gGcAzwJzKnM4qRqrDT33jHAUzHGzwFijN5/Uvkozf0XgUYhhAA0BL4BCiq3TKl6iTHmke6l1SlT1rKhhkstgS9KbM/I7FvXcyStm3W9r04GXqzQiqSaYa33XgihJXA4cDeSyktp/t3bAWgSQng9hPBOCOGXlVadVL2V5v67A+gAzALeA86NMRZWTnlSjVWmrKVOhZWzfsIq9q38WLvSnCNp3ZT6vgoh7E0Kl3pVaEVSzVCae+8vwKUxxuXpf+BKKgeluffqALsB+wIbAW+FEEbFGKdVdHFSNVea++8AYDywD9AW+F8IYUSM8duKLk6qwcqUtWyo4dIMYOsS261IafW6niNp3ZTqvgohdAIGAwfFGOdVUm1SdVaaey8XGJoJlpoD/UIIBTHGpyunRKlaKu1/c34dY/we+D6EkAd0BgyXpPVTmvvvROCGGGMEPgohfALsCIyunBKlGqlMWcuGuixuDNAuhNAmM7BtEPDMSuc8A/wyM8m8B7Awxji7sguVqpm13nshhNbAU8Dx/l9bqdys9d6LMbaJMW4bY9wWeAI402BJWm+l+W/O/wC9Qwh1Qgg5QHdgSiXXKVVHpbn/Pid1DRJCaAG0B6ZXapVSzVOmrGWD7FyKMRaEEM4mPQ2nNnB/jPH9EMIZmeN3Ay8A/YCPgMWkVFvSeijlvfc7oBlwZ6aDoiDGmJutmqXqoJT3nqRyVpp7L8Y4JYTwX2AiUAgMjjGu8vHNkkqvlP/2XQs8GEJ4j7RU59IY49dZK1qqBkIIQ0hPX2weQpgBXAXUhfXLWkLqMJQkSZIkSZLW3Ya6LE6SJEmSJElVgOGSJEmSJEmSysxwSZIkSZIkSWVmuCRJkiRJkqQyM1ySJEmSJElSmRkuSZKkKiGEsDyEML7E17ZrOPe7cvh5D4YQPsn8rHEhhJ5leI/BIYSOmdf/t9KxN9e3xsz7FP2+TAohPBtC2HQt53cJIfQrj58tSZIEEGKM2a5BkiRprUII38UYG5b3uWt4jweB52KMT4QQfgbcHGPstB7vt941re19QwgPAdNijH9Yw/knALkxxrPLuxZJklQz2bkkSZKqpBBCwxDCK5muovdCCP1Xcc6WIYS8Ep09vTP7fxZCeCtz7b9CCGsLffKA7TPXXpB5r0khhPMy+zYOITwfQpiQ2X9UZv/rIYTcEMINwEaZOv6ZOfZd5vtjJTuJMh1TA0MItUMIfwohjAkhTAwhnF6K35a3gJaZ9+kWQngzhPBu5nv7EEI94BrgqEwtR2Vqvz/zc95d1e+jJEnSmtTJdgGSJEmltFEIYXzm9SfAL4DDY4zfhhCaA6NCCM/EFduyjwGGxRj/EEKoDeRkzr0C2C/G+H0I4VLgAlLosjqHAu+FEHYDTgS6AwF4O4QwHNgOmBVjPBgghNC45MUxxstCCGfHGLus4r2HAkcBL2TCn32BXwMnAwtjjLuHEOoDb4QQXooxfrKqAjO/vn2Bv2d2TQX6xBgLQgj7AX+MMQ4MIfyOEp1LIYQ/Aq/GGE/KLKkbHUJ4Ocb4/Rp+PyRJkn5kuCRJkqqKH0qGMyGEusAfQwh9gEJSx04L4MsS14wB7s+c+3SMcXwIoS/QkRTWANQjdfysyp9CCFcAc0lhz77Av4uClxDCU0Bv4L/AzSGEG0lL6Uasw6/rReC2TIB0IJAXY/whsxSvUwjhiMx5jYF2pGCtpKLQbVvgHeB/Jc5/KITQDohA3dX8/J8Bh4UQLspsNwBaA1PW4dcgSZJqMMMlSZJUVR0LbAbsFmNcFkL4lBSM/CjGmJcJnw4G/hFC+BMwH/hfjPHoUvyMi2OMTxRtZDqAfiLGOC3T1dQPuD7TYbSmTqiS1+aHEF4HDiB1MA0p+nHAOTHGYWt5ix9ijF0y3VLPAWcBtwHXAq/FGA/PDD9/fTXXB2BgjPGD0tQrSZK0MmcuSZKkqqoxMCcTLO0NbLPyCSGEbTLn3EdaLtYVGAXsGUIomqGUE0LYoZQ/Mw8YkLlmY+BwYEQIYStgcYzxEeDmzM9Z2bJMB9WqDCUtt+sNFIVJw4BfF10TQtgh8zNXKca4EPgNcFHmmsbAzMzhE0qcughoVGJ7GHBOyLRxhRB2Xd3PkCRJWhXDJUmSVFX9E8gNIYwldTFNXcU5ewHjQwjvAgOBv8YY55LCliEhhImksGnH0vzAGOM44EFgNPA2MDjG+C6wC2lW0Xjgt8B1q7j8XmBi0UDvlbwE9AFejjEuzewbDEwGxoUQJgH3sJau80wtE4BBwE2kLqo3gNolTnsN6Fg00JvU4VQ3U9ukzLYkSVKphRVnXkqSJEmSJEmlZ+eSJEmSJEmSysxwSZIkSZIkSWVmuCRJkiRJkqQyM1ySJEmSJElSmRkuSZIkSZIkqcwMlyRJkiRJklRmhkuSJEmSJEkqM8MlSZIkSZIkldn/A0erXXOnzKLxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs = clf.predict_proba(x_test)\n",
    "preds = probs[:,1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
